Liam Betsworth

Nitendra Rajput
email: rnitendra@in.ibm.com

Saurabh Srivastava

Matt Jones
email: matt.jones@swansea.ac.uk

Audvert: using spatial audio to gain a sense of place

Keywords: Non-visual Navigation, Wayfinding, Auditory Display

We introduce Audverta system that facilitates serendipitous discovery and navigation through spatial audio; used to navigate and discover points of interest in large, unfamiliar indoor environments. Our main aim was to create a lightweight spatial audio display that can convey a sense of a place without complex point and select interactions. We conducted a preliminary study comparing two audio types to see which best suited sound localization and a study of Audvert used in a real world scenario. Our findings suggest that long continuous audio performs better than short intermittent audio for sound localisation. We also discover a change in behaviour when using the system, with a large percentage of users wanting to visit newly discovered shops after using the system. We discuss the findings and draw research conclusions.

Introduction

When a visitor enters a shopping mall for the first time, it is likely that they do not know where they are going. To maximize the available space in a mall, shops are often packed closely together, and depending on the specific layout of the shopping mall and the location of the visitor, it is also likely that not all shops are visible. All of these factors can make a large, unfamiliar indoor location a confusing place to be. Previous research on users' use of technology in unfamiliar places has shown that tourists often exhibit a wandering behaviour and then home in on serendipitous discoveries [START_REF] Brown | Tourism and mobile technology[END_REF]. To aid such activities, maps (physical and digital) are sometimes used, although as mentioned by McGookin et. al [START_REF] Mcgookin | Audio bubbles: Employing non-speech audio to support tourist wayfinding[END_REF], maps are not best suited to these kinds of activities due to changing contexts and the need to constantly refer to a map. We argue that tourist behaviour can be linked quite closely to visitor behaviour when in large and unfamiliar places such as shopping malls. It is easy to imagine a visitor to a shopping mall, wandering, homing in on anything of interest to them.

In an effort to aid such visitors, we have developed a system that attempts to facilitate navigation (homing in) and serendipitous discovery (wandering) using only spatial audio. Audvert is a mobile application that attempts to give a user a sense of place. Conventional maps are good at summarising what points of interest are available to a user in the nearby environment, but Audvert attempts to give the user a sense of what is actually around them by delivering spatial audio feedback. When a user hears a point of interest (POI) played back through their headphones, the audio sounds as though it originates from the real physical location of that POI. A visitor may not always be able to see a shop, but with Audvert they can infer the direction and proximity from the audio feedback, enabling them to then look around to see possible pathways of getting there. Where existing audio navigation and discovery systems have simply focused on alerting a user to the proximity or direction of a POI [START_REF] Holland | Audiogps: Spatial audio navigation with a minimal attention interface[END_REF][START_REF] Jones | Ontrack: Dynamically adapting music playback to support navigation[END_REF][START_REF] Mcgookin | Pulse: the design and evaluation of an auditory display to provide a social vibe[END_REF][START_REF] Mcgookin | Audio bubbles: Employing non-speech audio to support tourist wayfinding[END_REF], Audvert is unique in that it is used indoors, layering useful, spoken information about points of interest with directional and proximity feedback. Audvert is a lightweight system that offers the user continual feedback, allowing the user to engage when they want. The following scenario, along with Fig. 1, illustrates how a visitor might use Audvert in a shopping mall.

Scenario.

Fred is at a large shopping mall but is unsure about which shops to visit. Putting on his headphones and opening the Audvert application, he holds his phone out in front of him (1). The Audvert app randomly picks a store from the mall and begins playing an audio clip containing the name and description of the goods and services available there. Fred can hear this information on his right side indicating that this particular store is in that direction (2). Deciding he is not interested in this shop, Fred waits a few seconds for the next random selection to be played. After listening to the name and short description of the second store, which is now being played in his left ear, Fred decides he wants to know more and signals this via a shake gesture with his phone (3). Fred now hears additional information and uses the direction the sound is coming from to guide him to the store (4). As he approaches the store, the amplitude increases.

Related Work

As inexpensive, powerful smart phones have become more popular, soundscape and auditory display technologies have reached the consumer domain. A large amount of research on these topics has focused on their deployment for museum exhibits [START_REF] Heller | Multi-user interaction in virtual audio spaces[END_REF] and outdoor, augmented-reality soundscape experiments [START_REF] Cater | Location aware interactive applications[END_REF]. In terms of its applications in way finding, earlier work has tended to focus on the audio modality as a basic means of A to B navigation [START_REF] Holland | Audiogps: Spatial audio navigation with a minimal attention interface[END_REF][START_REF] Jones | Ontrack: Dynamically adapting music playback to support navigation[END_REF], but more recently, researchers have focused on the specific problem of tourist way finding. McGookin et. al developed a system named Audio Bubbles [START_REF] Mcgookin | Audio bubbles: Employing non-speech audio to support tourist wayfinding[END_REF], "employing non-speech audio to support tourist way finding." The researchers argue that the way in which tourists navigate is not so well supported by traditional visual maps. We argue that this is also the case for unfamiliar indoor envi-ronments such as shopping malls. Our system similarly attempts to facilitate serendipitous discoverysomething that visual maps do not focus on. Audio Bubbles uses a Geiger counter metaphor to signify proximity to a POI, similar to the early audio navigation system, AudioGPS [START_REF] Holland | Audiogps: Spatial audio navigation with a minimal attention interface[END_REF]. In our design, we have chosen against using this common geiger counter metaphor. Instead, we use amplitude to give feedback of proximity. We believe this is a more natural mapping, as sounds naturally get quieter as they are further away. Unlike Audio Bubbles, we also incorporate directional feedback. We encode directional and proximity feedback in our audio recordings at runtime, similar to ONTRACK [START_REF] Jones | Ontrack: Dynamically adapting music playback to support navigation[END_REF], although using the full 360° for direction instead of simple left and right panning. By not using the Geiger counter metaphor, we are also able to use audio recordings such as shop descriptions.

Different kinds of audio feedback for auditory displays have been explored in depth by Vazquez-Alverez et. al [START_REF] Vazquez-Alvarez | Auditory display design for exploration in mobile audio-augmented reality[END_REF]. One auditory display system that uses spoken word is PULSE [START_REF] Mcgookin | Pulse: the design and evaluation of an auditory display to provide a social vibe[END_REF]. PULSE turned local Twitter tweets into spoken word using speech synthesis. When users approached the physical location where a tweet was composed, it would play to the user. Audvert is again, different from PULSE as we use spatial audio that takes distance into account. PULSE was designed to provide the user with a social vibe -"an intrinsic understanding of the people, places and activities around their current location." We strive to achieve a similar sense of place in Audvert, but through using pre-recorded audio content relevant to the shopping mall.

One problem that can occur when too many spatial audio streams are playing concurrently is increased cognitive load [START_REF] Vazquez-Alvarez | Eyes-free multitasking: the effect of cognitive load on mobile spatial audio interfaces[END_REF]. To combat this problem, we have chosen only to play one spatial audio stream at a time. In doing this, we also make the process of stream selection easier for the user. Most audio displays make use of a bearing-based selection technique (e.g. [START_REF] Marentakis | Effects of feedback, mobility and index of difficulty on deictic spatial audio target acquisition in the horizontal plane[END_REF]), but this technique has its problems. When the number of selectable entities (audio sources in this case) increase, the bearing gap between each decreases. According to Strachan and Murray-Smith [START_REF] Strachan | Bearing-based selection in mobile spatial interaction[END_REF], effective selection was maintained down to around a 20° separation with a 10° target width. As Audvert only ever uses a single audio stream, we use a simpler interaction techniquea shake gesture to select or deselect the audio stream. We have chosen to use a gesture over a button, as previous research on gestures as a means of control for mobile devices has shown that they are a reliable, eyes-free interaction technique [START_REF] Pirhonen | Gestural and audio metaphors as a means of control for mobile devices[END_REF]. Further research has also been carried out on the practical applications and social acceptability of mobile gestures in the real world [START_REF] Rico | Usable gestures for mobile interfaces: evaluating social acceptability[END_REF]. We believe the shake gesture is a simple and discreet gesture that most users will be able to use and will be comfortable with.

In summary, Audvert is novel in that it is an attempt at indoor audio navigation and discovery, not only providing proximity and directional feedback like previous systems, but also giving useful spoken information. It also tries to simplify the task of spatial display selection by making only one choice available at a time.

Audvert

Audvert is a prototype system designed to give users a better sense of the place that they are in using only spatial audio. To use the system, a pair of headphones and the Audvert application is required. When using Audvert, sounds appear to originate from their actual physical locations. A user should be able to infer proximity through amplitude (closer is louder) and direction through the panning of sounds. Spatial audio feedback in the system is constantly changing due to lightweight interactions such as the user walking around (changing their location in the system) or the user changing direction (changing their listening orientation). Audvert uses a digital embedded compass, so as the user rotates, sounds continue to appear as though originating from the same physical location in space. With regards to location tracking, we decided upon a Wizard of Oz study methodology for Audvert. Users are not told that their location is entered manually elsewhere, so it is fair to say that users believe that their location is being automatically tracked. We use a separate purpose built system where a researcher may tap a map to update the location of a user. This Wizard of Oz approach was chosen as it offers a high accuracy with little complication. GPS was not a viable option as the system was used indoors. In reality, this kind of system would most likely work indoors with WiFi localisation. When considering the user interaction with the prototype system, we decided that it should facilitate three separate functions, all relative to tourist way finding [START_REF] Brown | Tourism and mobile technology[END_REF] -to aid serendipitous discovery of POI (wandering), to deliver useful information about them and to offer users the ability to navigate towards them if they wished to do so (homing). To facilitate serendipitous discovery, Audvert randomly chooses points of interest in the local environment, playing them back one at a time to the user. Useful information about the points of interest is spoken in the audio recordings (sales, offers and general information about the shop). Once a user has selected a POI, Audvert facilitates navigation through continually playing spatial audio for that POI.

User Evaluation

Two studies were conducted to test Audvert. The first was used to determine audio preference and performance. The second study gave participants the opportunity to use the system fully as they would do in a real life scenario. Both studies were conducted in a shopping mall in New Delhi, India.

Prior to the evaluation, early testing with users alerted us to the issue of users pointing the device in a different direction to their orientation. Because of this, a static arrow was added to the GUI as an affordance, so that users would remember to point the device in front of them at all times. There were also instances where users would forget the name of the shop they were currently listening to. For this reason, the name of the current shop the user is listening to was also added to the interface.

Preliminary Study: Cube Placement

Before embarking on a usability/user experience study of Audvert, we wanted to ensure we picked the optimal type of audio for use within the system. With this in mind, we recruited 24 participants (18M, 6F, 16-37 years) for a between-subjects study testing the effectiveness of two different types of audio; short intermittent and long continuous. We define short intermittent as audio clips that do not have continuous background sound and are short in length (i.e., purely spoken word, 5-6 seconds long). Long continuous clips contain continuous background sound and are longer in length (i.e., spoken word with a musical backing track, ~25 seconds long). Our goal in this preliminary evaluation was to determine which of these two types of audio provided users with the most accurate path to the audio source location. This was achieved by playing five audio sources and asking each participant to place five paper cubes on a map of the shopping mall that best matched the location of where sounds were originating from. The map was A3 in size and each cube was 2cm 3 . Direction scores were calculated depending on the cube placement angle of error. Scores from 10 to 0 were given for 22.5° segments. Proximity scores where calculated depending on the cube placement error in centimeters (distance from user). None of the participants we recruited had any prior knowledge of the shopping mall.

The results of this evaluation showed that on average, participants using long continuous audio performed better in terms of perceiving both proximity (p < 0.0068; Unpaired t-test; 1.8cm average difference) and direction (p < 0.0018; Mann-Whitney; 11.4/50 average score difference). We therefore decided that short intermittent audio should be used as a brief alert of a shop's existence (when a shop is unselected), and long continuous audio should be used to give a person more information about a shop and allow a user to navigate towards it if they wish (when a shop is selected).

Usability & User Experience Study

The purpose of this study was to gain a better understanding of the usability and user experience offered by the system. We wanted to discover if Audvert could be used to navigate towards a shop that a user does not know the location of, and more generally, how a user feels about their surroundings when using the system. In search of answers, we attempted to evaluate the system in a real usage scenario, such as when a visitor is walking in a shopping mall and wants to know more about the shops there.

Participants. Participants of this study were again, real visitors to the shopping mall. We recruited 19 participants (10M, 9F, 17-47 years), none of which had taken part in the preliminary study. Approximately 3 were regular visitors to the mall. All participants could speak and understand English. No participant reported hearing problems. All participants had used a mobile phone before, but not all had used a smartphone.

Task & Measurements.

Participants of this study were expected to explore the shopping mall using the system. Each participant was personally allowed to choose what he or she used the system for. Qualitative data on user experience with the system was gathered through a post-study interview. Questions asked in this interview were: Has the system given you a better or worse understanding of the shop locations? How would you describe your sense of place after using the system? (Level of comfort and familiarity with the place) Did you hear any shops that you have not visited before? (Would you consider visiting one of them now?) Procedure. Participants were recruited individually as they walked through the shopping mall and were given a short introduction to the Audvert application. Participants were asked to explore the shopping mall using Audvert. As participants moved around the shopping mall, a researcher stood around 20 meters away and updated the participant location every few seconds. While exploring, participants were free to interact with the system as they wished. At the end of each study session, the participant was asked a series of questions in a post-study interview regarding their experience with the system. No incentive was given for completing the study.

Results.

The following section details the responses given to the questions asked in the post-study interview. There were some participants that could not decide upon an answer to some of the questions. These have been included as N/A in the results. Percentages are relevant to the number of participants that answered each question.

Response

Question

Better Unaffected Worse N/A Has the system given you a better or worse understanding of the shop locations? 79% (15) 5% (1) 16% (3) 0

How would you describe your sense of place after using the system?

80% (12) 7% (1) 13% (2) (3)

Yes

Maybe

No Would you consider visiting a new shop you discovered using the system? 63% (10) 31% (5) 6%

Table 1. Quantitative results from questions asked in the study.

Informal Observations & Feedback. It became apparent in the study that not every participant was selecting a shop by shaking the device. By avoiding this function, the participant was not able to gain extra information about a shop (a long description)the system however, is still usable without this function. As Audvert provides no visual feedback of where the user is in the system, one participant was unsure whether a shop was selected or unselected at any point in time. This participant carried on for a further two minutes and after this period appeared to be much more comfortable with the system. One participant that successfully used the shake gesture to activate a shop was seen attempting to navigate towards the selected shop. This participant attempted to follow the direction of sounds, and did so successfully by finding the shop. The participant mentioned that they knew they were going in the correct direction because they could hear the sounds getting louder as they got closer to the shop. This person said that they had never visited the shop before, and that they had no prior knowledge of its location. There was also another situation where a participant used the varying volume of sounds to navigate towards a shop. This participant had selected a shop, walked in one direction and then realised that the volume was decreasing -inferring that they were heading in the wrong direction. The participant then turned around to face the correct direction and successfully found the shop that they had selected.

Discussion

Most participants enjoyed the experience, claiming that the system appeared to be 'very accurate' and that the direction sounds came from was the same direction that they could see the shops. A large portion (79% (15)) of participants said that they had a better understanding of the location of shops after using the system. One participant who claimed a worse knowledge of shop locations said that it was "difficult to differentiate between the distances of each shop." Another participant agreed, finding it "difficult to tell the distance to a shop", but claiming the "direction was good." Although 79% of participants said that their understanding of shop locations had improved, 1/3 of these (5 participants) claimed that they could either infer direction or distance, but not both. In most of these 5 cases, participants could only infer direction (4 participants). This is an interesting result, as without knowing both, one would imagine it being difficult to pinpoint an exact location. These participants obviously believed that knowing only direction or proximity was sufficient for knowing the location of a shop. This strengthens the argument for spatial audio as a navigation aid.

A similarly high percentage (80% ( 12)) claimed that they had a better sense of place after using Audvert. There were 5 participants who did not answer this question, unable to differentiate between knowing the locations of shops and a sense of place. One participant said that the system gives a "good sense of the area. I could tell what kinds of shops and products were available." Another participant argued that the system gave a bad sense of place as there were "lots of shops and I only heard a few." Although a similar percentage said they had a better sense of place and an improved understanding of shop locations, not every participant who had an improved understanding of shop locations had a better sense of place, showing that the two weren't regarded as the same question by participants.

80% of participants heard a shop they had not visited before, of which, 63% (10) said they would definitely consider visiting that shop and 31% (5) said they might visit now. There were 4 participants who had previously visited every shop that they heard when using the system. Only one participant decided that they would not visit a new shop they had discovered, saying that they are the "kind of person that comes to the mall for one thing." There does appear then, to be a compelling change in behaviour, where the system seems to encourage participants to visit new, serendipitous discoveries that are made. Generally, most participants explained that they would like to visit these shops because of sales and discounts mentioned in the audio recordings. Participants who said they might visit a new shop often said that they would do so if more detailed information about a shop was available. Judging by this feedback, it seems that the content is an integral factor of this change in behaviour.

The majority of participants liked the shake gesture. Two participants explicitly mentioned that they would "prefer the shake over a button." In contrast, two participants said that they were not comfortable with the shake gesture, preferring a button. One participant's concern came from not wanting to shake their expensive mobile phone. To satisfy all users, a multimodal approach could be introduced, where the user chooses whether the interaction occurs onscreen or through a gesture.

Conclusions

We have introduced Audvert to illustrate the potential of a lightweight system that facilitates navigation and serendipitous discovery of large indoor spaces using spatial audio. While the idea of an audio display for navigation and discovery is not a new one, we argue that the novelty of our approach lies within its indoor application and the feedback and selection techniques employed. We use amplitude and directional feedback to manipulate useful, spoken audio information about a point of interest in real time. Audvert has also introduced a simplified selection technique for spatial displays through using a single active element and a simple shake gesture to select.

Our user evaluations have shown that Audvert can convey useful spatial audio feedback, and that it is capable of helping users towards and discover new points of interest. We saw multiple examples of users successfully arriving at a shop and for the majority of participants, we witnessed a change in behaviour with participants claiming that hearing new shops made them want to go and visit there.

Fig. 1 .

 1 Fig. 1. User interaction in Audvert (explained in scenario above).


Tim Harries

Parisa Eslambolchilar

Chris Stride
email: c.b.stride@sheffield.ac.uk

Ruth Rettie
email: rettie@kingston.ac.uk

Simon Walton
email: simon.walton@oerc.ox.ac.uk

Walking in the Wild -Using an Always-on Smartphone Application to Increase Physical Activity

Keywords: walking, feedback, norms, app, active-lifestyle, social sharing 1

This multidisciplinary paper reports on a large-scale field trial, designed and implemented by a group of social scientists, computer scientists and statisticians, of a new smartphone-based app for the promotion of walking in everyday life. The app, bActive, is designed for a more diverse range of users than the typical active-lifestyle app, since it requires neither additional equipment nor a great deal of commitment to exercise. As a result, it can raise awareness of walking and promote walking amongst those with only a casual or hesitant engagement with the topic. The 6-week randomised controlled trial with 22-40 year-old male participants (N=152) indicates that bActive prompted users to increase the amount of walking they did by encouraging them to value and increase walking that is incidental to normal everyday activities. Longitudinal data analysis showed that use of the app increased walking by an average of 64% but did not find any evidence to suggest that the inclusion of comparative social feedback improves the impact of such apps on male participants.

Introduction

Walking is generally considered enjoyable, relaxing [START_REF] Pooley | Understanding Walking and Cycling: Summary of Key Findings and Recommendations[END_REF], beneficial for general health [START_REF] Andersen | Physical Activity and Health: Even Low Intensity Exercise Such as Walking is Associated with Better Health[END_REF] and helpful for the prevention of obesity and chronic disease [START_REF] Maclellan | The Application of a Physical Activity and Location Measurement System to Public Interventions to Promote Physical Activity[END_REF]. It is "readily repeatable, self-reinforcing and habit-forming" [START_REF] Morris | Walking to health[END_REF] and is the most widely accessible type of exercise because it is inherently safe, requires no special skills, location or equipment and can easily be included in domestic and work routines [START_REF] Pooley | Understanding Walking and Cycling: Summary of Key Findings and Recommendations[END_REF]. However, most residents of advanced economies take far less than the daily total of 10,000 steps generally recommended for good health [START_REF] Bohannon | Number of Pedometer-Assessed Steps Taken Per Day by Adults: A Descriptive Meta-Analysis[END_REF][START_REF] Choi | Daily Step Goal of 10,000 Steps: A Literature Review[END_REF][START_REF] Iwane | Walking 10,000 Steps/Day or More Reduces Blood Pressure and Sympathetic Nerve Activity in Mild Essential Hypertension[END_REF][START_REF] Mccormack | Physical Activity Levels of Western Australian Adults 2002: Results from the Adult Physical Activity Survey and Pedometer Study[END_REF][START_REF] Schneider | Effects of 10,000 Steps per Day Goal in Overweight Adults[END_REF].

Unlike most previous apps aimed at increasing physical activity, the one described here, bActive, requires no special equipment and does not need to be activated by the user in order to track activity. This makes it easier to acquire (users only have to download the app and do not need to purchase any physical device), easier to use and more suitable for those with a passing or tentative commitment to becoming more active. The only demand made by bActive is that users engage with the feedback. This is encouraged firstly by their being able to do so at their convenience and, secondly, by the inclusion of trend data that is likely to engage their interest.

The bActive app collects data from the phone's built-in accelerometer and utilises an always-available display to deliver information to users about the number of steps they have taken. The presentation of this information was designed to increase walking activity in three principal ways. First, users are made more aware of the exercise involved in purposive 'walks' and that inherent in the walking involved in day-to-day activities such as shopping and work. Second, users are able to track their own activity over time. Third, they can be offered the chance to compare their activity levels with the average activity levels of others.

This study compared a control condition (no feedback) with the use of feedback limited to a user's own walking and the use of comparative data i.e. a social norms approach [START_REF] Cialdini | Social Influence: Compliance and Conformity[END_REF][START_REF] Harries | Is social norms marketing effective? A case study in domestic electricity consumption[END_REF]. The social norms approach has been successfully used in fields as diverse as alcohol abuse, sexual behaviour, the payment of tax debts and domestic electricity consumption [START_REF] Burchell | Marketing Social Norms: Social Marketing and the 'Social Norm Approach[END_REF] but has not previously been applied to exercise or delivered by a smartphone app. The approach has two main elements. First, on the assumption that forces of conformity encourage people to emulate social norms, it provides individuals with information about the average behaviours of a group of salient others (the descriptive norm). Second, to avoid encouraging change in a negative direction (e.g. regression towards a lower activity level), some social norms practitioners provide users with moral approval for 'good' behaviour (the injunctive norm) [START_REF] Cialdini | A Focus Theory of Normative Conduct: Recycling the Concept of Norms to Reduce Littering in Public Places[END_REF][START_REF] Schultz | The Constructive, Destructive, and Reconstructive Power of Social Norms[END_REF].

A meta-analysis of the effectiveness of this approach in its main area of application, alcohol abuse by students, is presented by Bosari and Carey [START_REF] Bosari | Descriptive and Injunctive Norms in College Drinking: A Metaanalytic Integration[END_REF]. In this domain, the approach usually begins with survey research on respondents' own alcohol consumption and their assumptions about that of other students in the same university. Where this reveals a tendency to overestimate alcohol consumption, the discrepancy is conveyed to the student population of the university via poster campaigns. In another example, a randomized controlled trial of a US programme that posted reports containing social norms information with households' electricity bills found that this intervention reduced consumption by around 2% [START_REF] Allcott | Social Norms and Energy Conservation[END_REF].

Related Work

Pedometers use piezoelectric accelerometers to count the number of steps walked and can be worn on the body or carried in a pocket. Pedometers have long been of interest for their ability to encourage more active lifestyles [START_REF] Baker | Using Pedometers as Motivational Tools: Are Goals Set in Steps More Effective Than Goals Set in Minutes for Increasing Walking[END_REF][START_REF] Dewa | Walking for Wellness: Using Pedometers to Decrease Sedentary Behaviour and Promote Mental Health[END_REF]. However, exercise promotion programmes have typically used pedometers alongside other resource-intensive activities such as classroom training and face-to-face sessions [START_REF] Bravata | Using Pedometers to Increase Physical Activity and Improve Health: A Systematic Review[END_REF][START_REF] Kang | Effect of Pedometer-Based Physical Activity Interventions: A Meta-Analysis[END_REF], so little empirical data exists regarding the use of pedometers in more natural contexts.

Pedometers exist either as devices dedicated to the measurement of exercise, or as embedded features in other equipment such as mobile phones. This difference has implications for accuracy and usability. Accuracy is highest amongst dedicated pedometer-devices, where it exceeds 96% at speeds of over 3 miles per hour (mph), dropping to 74%-91% at 2-3mph and 60%-71% at below 2mph [START_REF] Baker | Using Pedometers as Motivational Tools: Are Goals Set in Steps More Effective Than Goals Set in Minutes for Increasing Walking[END_REF]. However, users have to be committed enough to fitness and lifestyle-change to purchase the pedometer and remember to wear or carry it. This, and questions of fashion, design and convenience, can deter some people from using such devices [START_REF] Consolvo | Design Requirements for Technologies that Encourage Physical Activity[END_REF]. In contrast, mobilephone pedometer apps have the advantage of being embedded in equipment that people already own and keep on their person and their in-built display and communication capabilities allow users to share their feedback with others. Hence, smartphones are increasingly being used to address the problem of sedentary lifestyles [START_REF] Consolvo | Activity Sensing in the Wild: A Field Trial of Ubifit Garden[END_REF].

Goal Setting

Goal-setting and performance feedback, which most generations of smartphones make convenient and easy,1 are important influences on individual behavior [START_REF] Locke | Building a Practically Useful Theory of Goal Setting and Task Motivation: A 35-Year Odyssey[END_REF] and are generally considered key features of technologies intended to encourage physical activity [START_REF] Consolvo | Design Requirements for Technologies that Encourage Physical Activity[END_REF].

A number of factors influence the effectiveness of a goal. Firstly, it must be accepted by the individual and not in conflict with their other goals [START_REF] Locke | New Directions in Goal-Setting Theory[END_REF]. Secondly, increased difficulty is said generally to increase motivation [START_REF] Locke | New Directions in Goal-Setting Theory[END_REF], possibly because of the greater potential self-satisfaction that accrues to the user on achievement of the goal [START_REF] Steel | Integrating Theories of Motivation[END_REF]. However, goals must not be considered unachievable [START_REF] Locke | New Directions in Goal-Setting Theory[END_REF] and though failure to attain a goal can increase motivation [START_REF] Brusso | The Impeding Role of Initial Unrealistic Goal-Setting on Videogame-Based Training Identifying Underpinning Processes and a Solution[END_REF], it can also have the opposite effect [START_REF] Ilies | Goal Regulation Across Time: The Effects of Feedback and Affect[END_REF]. Thirdly, the discounting of future benefits leads to the argument that short-term goals are more effective than longer-term ones [START_REF] Steel | Integrating Theories of Motivation[END_REF].

One approach to determining physical activity goals is demonstrated by Chick-Clique [START_REF] Toscos | Chick Clique: Persuasive Technology to Motivate Teenage Girls to Exercise[END_REF] and UbiFit Garden [START_REF] Consolvo | Flowers or a Robot Army? Encouraging Awareness & Activity with Personal, Mobile Displays[END_REF][START_REF] Consolvo | Activity Sensing in the Wild: A Field Trial of Ubifit Garden[END_REF], in which users set their own daily step-count goals. However, this approach runs the risk that inexpert users will set goals that are either too difficult or too easy and that do not, therefore, provide optimal motivation [START_REF] Schneider | Effects of 10,000 Steps per Day Goal in Overweight Adults[END_REF]. In contrast, in Fish'n'Steps [START_REF] Lin | Fish'n'Steps: Encouraging Physical Activity with an Interactive Computer Game[END_REF] goals are set at a modest level by automatically using baseline step-counts as reference points and taking the findings of previous studies as a guide to what users could reasonably be expected to attain. Furthermore, Fish'n'Steps breaks longer-term goals down into proximal, daily, sub-goals. No research has been done to directly compare these two, contrasting, approaches.

Social Sharing

Mechanisms that facilitate social influence are also often considered essential for devices that encourage physical activity [START_REF] Consolvo | Design Requirements for Technologies that Encourage Physical Activity[END_REF]. A number of smartphone apps include this facilitye.g. [START_REF] Consolvo | Design Requirements for Technologies that Encourage Physical Activity[END_REF][START_REF] Toscos | Chick Clique: Persuasive Technology to Motivate Teenage Girls to Exercise[END_REF][START_REF] Lin | Fish'n'Steps: Encouraging Physical Activity with an Interactive Computer Game[END_REF]. In Fish'n'Steps, for example, each user is presented with a fish avatar whose growth, emotional state and behaviour reflect the number of steps the participant takes each day. The avatars of each group of users are displayed on a screen in an area shared by all its members (e.g. the social space in an office) and also on users' personal websites. A second example is MapMyWALK,2 whose popularity is demonstrated by the fact that it had been downloaded from Android App Shop 250,000 times by July 2012. This app allows goals, routes, distances and walking speeds to be shared with friends and family members via email and social media.

Evidence on the effectiveness of these apps is mixed and small sample sizes cast doubt over its validity. For example, although Fish'n'Steps is reported to have caused some participants to increase their step-count, the unhealthy fish avatars that result from low activity levels caused others to drop out of the study. Similarly, although Consolvo et al [START_REF] Consolvo | Design Requirements for Technologies that Encourage Physical Activity[END_REF] claim that social comparisons influenced their (all female) participants, the report of their three-week pilot of Houston with friendship groups of young females (N=13) reveals that the sharing of data did not have any significant impact on step counts. Finally, in a trial of Chick-Clique (N=7), group performance was reported by the participants (13-17 year-old girls) as the most "powerful method of changing behavior" [38; p1877], but the sample was too small to test this claim.

3

The bActive App

Although the design of the bActive app drew on the principles detailed in the previous section, it differs in three key ways from most of the apps described. Firstly, no assumptions are made about users' willingness to spend time and effort tracking their activity levels. Users simply have to download the app and carry their mobile with them in a trouser pocket. There is no requirement for additional equipment such as pedometers or foot pods, or for the data entry required for diarisation. While motivated users may be prepared to carry additional devices to measure specific physical activities, they are less attractive to those who are ambivalent about the benefits of measurement or their ability to become fitter and healthier [START_REF] Consolvo | Design Requirements for Technologies that Encourage Physical Activity[END_REF], and hence are unlikely to promote ubiquitous use. Secondly, unlike apps that activate only when users notify them at the start of an exercise event (e.g. MapMyWALK), bActive measures activity continually and without the need for any user action. Users are therefore rewarded with their activity data without having to make any initial effort or remember to switch the recording function on and off. This means, in addition, that rather than focusing uniquely on intentional exercise events such as hikes or walks, bActive also measures the exercise inherent in routine activities such as shopping, walking children to school or moving around at work. As a result, the emphasis is on the adoption of a healthy lifestyle, rather than on participation in walking as sport or recreation. This too is one of the reasons for not using GPS in bActive, for much incidental walking (e.g. shopping-and work-related walking) occurs in geographically confined spaces and is therefore less amenable to measurement by GPS.

The third difference concerns goal-setting. In bActive, formal goal-setting, training and coaching elements are replaced by self-generated, informal targets that result from a user's engagement with the feedback information. As a result, rather than feeling that they are engaging in a formalized exercise program, users are allowed to respond to this information in whatever way they wish. As argued by Thaler and Sunstein [START_REF] Thaler | Nudge: Improving Decisions about Health, Wealth, and Happiness[END_REF], behavioral feedback forms part of the choice architecture that nudges behavior. In this case, the bActive feedback nudges users to walk more. The only action required of them is that they occasionally bring the app to the foreground by clicking on the bActive icon, and this is subtly prompted by the presence of the bActive icon on the phone screen (see Figure 1).

To engage people who are initially less committed to increasing their activity, it is particularly important that bActive is seen as interesting and fun to use. Learning from Fish'n'Steps and UbiFit Garden, it uses non-literal, light-hearted visual representations of behaviour. It also provides trending information (as in Fish'n'Steps, UbiFit Garden, Into and Houston); gives positive reinforcement (learning from UbiFit Garden and Houston and from the problems experienced by Fish'n'Steps), and, like Houston, Chick Clique, UbiFit Garden and Into, provides opportunities for users to reflect on their own activity. Finally, like the social gaming and social data sharing features of Fish'n'Steps, Houston, Chick-Clique and Into, the social norms information within bActive is designed not only to prompt increased walking, but also to encourage engagement with the feedback.

Design Philosophy

To test the efficacy of individual and social norms feedback, three different versions of the app were created: a 'null' version for participants in the control condition, which provided no user interface and gave no access to feedback; a 'partial' version for those in the individual condition, which displayed individual data only; and a 'full' versions for those in the social norms condition, which displayed both individual data and group averages. This section focuses on the version provided to those in the latter condition, for this was the most complex in terms of design.

Developed for Android 2.3, bActive incorporates automatic step counting alongside on-demand real-time and historic feedback of the number of steps taken by the user and the average of a group of other users. It also logs the frequency with which users open the app and the length of time the app is open on the display. The app is intended to be used on-the-go, so data clarity is emphasized and users are not asked to perform any retrieving or filtering tasks. To verify ease of use, the app was piloted twice and testing performed in a variety of outdoor conditions.

The app features four views of the data: Today, Yesterday, Past Week and History (Figure 1). The Today screen shows progress for the current day in terms of steps, distance and calories (calculated for the target demographic of young men) [START_REF] Groves | Using Pedometers as a Valid Method of Determining Physical Activity Intensity Level[END_REF][START_REF] Tudor-Locke | Comparison of Pedometer and Accelerometer Measures of Free-Living Physical Activity[END_REF]. Values for the group average are displayed alongside those for the individual user. To facilitate rapid review of progress, an animated avatar representing the user (the green walking figure) is shown either behind, in front of or alongside an animated group of avatars (the grey walking figures) that represent the average activity for those in the comparison group. A banner just below the avatars displays a feedback message that varies according to how the individual's performance compares to that of the group. For those above average, it toggles between a descriptive norms message (e.g. "Your activity levels are above average") and an explicitly evaluative injunctive norms message (e.g. "

Well done, keep it up!"). If the individual's activity is below average, the banner displays a single descriptive norms message (i.e. "Your activity levels are below average"). The Yesterday screen is identical to the Today screen, but gives the previous day's results. The Past Week screen (Figure 2) displays a line graph depicting activity levels for the previous seven days, including averages for the group and for the most active 20%. Identical to the Past Week screen in format, the History screen allows users to swipe back and forth between different weeks.

Client/Server Architecture

The app uses the handset's mobile data connection to send users' activity levels and app usage to a central MySQL, and retrieves the group's activity average from that database.

Step data is sent from the phone by a background Android service that transmits step data every two hours. This enables data to be updated asynchronously as users visit the screens, and ensures that they are shown up-to-date information. If lack of connectivity prevents data transmission, the app reduces the delay for activity transmission from two hours to one hour until a successful link is established. Data on use of the app is transmitted every four hours.

Fig. 2. The Past Week screen as seen by those using the full version of the app

A delay-based caching mechanism decides when particular elements of data on a user's device need to be updated and only requests fresh data when required. Meanwhile, the app provides the user with an indication of how much data it is using.

Activity Monitoring

The app's activity monitoring system is implemented as an Android service. Designed to be as autonomous as possible, it starts upon device boot and automatically recovers from unexpected crashes. On starting, it registers itself as a foreground service in order to prevent the Android process scheduler from hibernating the process when there are non-severe memory requests from other apps. An entry on the Android notification bar to indicate to the user that the app is running (Figure 1).

When the service is running, each accelerometer reading arriving as an (x, y, z) array is treated as a vector of magnitude m. Instead of computing the true vector magnitude, the app simply computes m 2 as x 2 + y 2 + z 2 -G 2 , where G is Android's accelerometer constant for Earth's gravity. The step counting algorithm is based on that of Mladenov and Mock [START_REF] Mladenov | A Step Counter Service for Java-Enabled Devices Using a Built-in Accelerometer[END_REF], which treats accelerometer values as graph y-values over time.

Keep-Alive / Battery Conservation Strategy

To preserve battery life, the Android power manager normally puts the CPU to sleep when the device screen is turned off through display timeout. Events such as phone calls and activated alarm clocks can turn the CPU and/or screen back on.

To ensure that the device captures a user's activity levels throughout the day without adversely affecting battery life, bActive uses a combination of two Android concepts: WakeLocks 3 and Alarms. Due to inefficient development of WakeLocks since Android 2.2, 4 a power strategy was used that allowed the CPU to sleep partially dur-ing periods of inactivity rather than reading the accelerometer at all times. The Android Alarm Manager wakes the phone from a low-power state once every 30 seconds and takes around twenty measurements from the accelerometer at a low frequency. If any of the readings are interpreted as representing a step, the accelerometer frequency is set to high and the device continues monitoring user activity until one minute after the final step is detected. At this point, the WakeLock is released, allowing the device to sleep for a further 30 seconds. Although essential for power saving, this strategy has the disadvantage that short bursts of activity (e.g. walking around a kitchen while cooking) will sometimes be missed if they are dispersed across periods of inactivity. This strategy may not be required in future generations of Android OS if the current issue with the WakeLocks is addressed effectively.

4

The Trial

Funded by the Research Councils UK Digital Economy Programme as part of the CHARM project, the six-week trial of the bActive app was conducted between October and December 2011 with 152 participants from Bristol, UK. Following the principles of a randomised controlled trial, participants were randomly assigned to one of a control condition (no feedback and no access to the interactive elements of the app), an individual condition (feedback on their own walking onlysee Figure 3) and a social norms condition (feedback that also included social norms data). On-street recruitment was conducted by a market research agency in twenty locations spread across Bristol. The incentive for participation was the study phone, the HTC Desire-S, which participants were able to keep. Recruits were told that the purpose of the study was to measure the amount of walking people did. Importantly, and unlike in most previous studies, participants were not asked to walk more. Each recruit had to agree to put his current SIM card into the phone, use it as his main mobile and carry it in his trouser pocket for the duration of the study. To ensure that users in the social norms condition were able to compare themselves to a group of broadly similar people, it was important to focus on a specific segment of users. Although the study could have been conducted with either men or women, it was decided to focus on men because of the need for the study phones to be carried in trouser pockets and the likelihood that women would find it more difficult to comply with this requirement. The age-range of 22-40 was chosen in order to avoid the health risks associated with the over-exertion of older users.

Of the 152 participants, 78% were in employment, 14% were students and 8% were unemployed. Of those in employment, 50% were in sedentary jobs (e.g. office workers), 44% were in moderately active jobs (e.g. teachers) and 6% were in very active occupations (e.g. postal workers). Meanwhile, 59% of the participants said that they regularly engaged in sport (about the same as the national average for this demographic [START_REF] Jones | Analysis of the Taking Part Survey[END_REF]) and 63% owned some form of motorized vehicle.

The intention of the research team had been to collect a week of baseline walking data prior to the feedback phase of the trial, but a technical malfunction during this initial week rendered the data unusable, so no baseline comparison was possible.

During the 6-week feedback phase of the trial, participants were sent regular emails and SMSs reminding them to keep the phones in their pockets; with further prompts sent to those whose phones had not sent data for more than a day. In addition, those in the individual and social norms conditions were sent a weekly motivational text message (e.g. "Walking is one of the best activities for your health. How much are you doing? Check the app!")

During the trial, exercise and app use data was collected on a daily basis. Specific variables collected were the number of steps taken by each participant and, for those in the two experimental conditions that were provided with some kind of feedback, the number of times per day they activated the bActive app. In all, the resulting dataset consisted of up to 42 daily observations for each of the 152 respondents across the 6 week period of the trial (6,214 observations in total).

Prior to the trial and at its close, an online questionnaire collected demographic data and potentially confounding variables such as prior use of smartphones, patterns of physical activity, attitudes to physical activity and perceived impacts of the trial.

Subsequent to the trial, two waves of interviews were conducted with trial participants. Sampling criteria for wave-1 (N=7), conducted one to two months after the trial by a member of the research team, were feedback condition and self-reported changes in walking behaviour. For wave-2 (N=8), conducted ten months after the trial, sample selection focussed on those still using the app in April 2012four months after the end of the trialand those with a below average total number of steps. Participants were approached by telephone and offered a £20 incentive. The number of participants that had children living with them 2 The number reporting that their walking had increased because of the study (+), stayed unchanged (0) or decreased (-) 3 The number in the social norms condition reporting that during the study their step-count had usually been above average (A); below average (B), varied between above and below average (C)

The trial set out to test three hypotheses:

 H1those with access to feedback will have higher step-counts than those in the control condition  H2those in the social norms condition will have higher step-counts than those in the individual feedback condition  H3those in the social norms condition will use the app more often than those in the individual feedback condition

Analysis Method

Given the structure of the data, which had multiple daily observations nested within each participant, longitudinal multilevel modelling [START_REF] Singer | Applied Longitudinal Data Analysismodelling change and event occurrence[END_REF] was used to test the three hypotheses. As described below, the same 3-stage series of analyses was performed for H3 (where the outcome variable was frequency of app use per day) as was used for H1 and H2 (where the outcome variable was number of steps per day).

First, an unconditional model with no predictors was run in order to calculate the ICC(1) statistic (the percentage of variance in scores over time attributable to differences between participants) and the variance to be explained at the within-and between-participant levels. Second, a fixed growth model was fitted to the data in order to estimate the shape and direction of changes to the outcome over time. To do this, linear and quadratic effects of time (i.e. days since the start of the study) were added to the model, together with a dummy variable for day of the week (with Sunday set as the reference category).

The third stage tested for variability of change between participants by allowing the coefficients of the growth parameters (i.e. the linear and squared effects of time) to become random (i.e. to vary by participant). Potentially confounding participant-level control variables were then added: i.e. marital status; number of children under 17 in household; employment status (30+ hours' employment per week; 8-29 hours' employment per week; carer/unemployed and in receipt of benefits; student, or self-employed); ownership and use of a motorized vehicle, motorcycle or bicycle (each coded separately), and previous ownership of a smartphone.

Finally, the effects of experimental condition and its interaction with the time-point were added to the model to see if they accounted for between-participant variation in the level of the outcome and its change over time. At each stage, model improvement was evaluated by testing the reduction in the model deviance and assessing the extra variance explained at both within-and between-participant levels.

There were two outcome variables. The first, Steps, was derived from the stepcount, which was log-transformed to negate the impact of outliers (participants with extremely high step-counts). The distribution of the second outcome variable, App Use (the number of times per day users activated the bActive app), was severely positively skewed. For this reason, data for the participants who could access the app interface (the two experimental conditions; N = 110) were analyzed using a multilevel generalized linear model, treating the error distribution as Poisson and applying a logarithmic link function. Given the large sample size at the level of the time-point, study day (N = 6214 for Steps; N = 4229 for App Use), a significance level of p < 0.0005 was used for assessing the acceptance or rejection of null hypotheses at this level. For participant-level effects (N = 152 for Steps; N = 102 for App Use) the more typical p < 0.05 level of significance was applied. Where hypotheses were directional, one-tailed tests were used.

The interviews and focus groups were transcribed, coded using Atlas-ti and analysed using a combination of thematic and discourse analysis.

Results

Impacts of feedback on step-counts

An assessment of the variation in Steps revealed a high level of clustering within participants, with an ICC(1) statistic of 0.33 indicating that a third of the total variation was due to between-participant differences. The introduction of linear and quadratic effects of time alongside dummy codes for day of the week explained a statistically significant but small 4% of within-participant variance and almost no betweenparticipants variance. Tests of fixed effects coefficients indicated that of these three predictors (the linear effect of time, the quadratic (curvilinear) effect of time and day of the week), the third was the primary explanatory variable. Since quadratic change offered no improvement over a simple linear effect, it was dropped from the model. There was evidence that the linear effect of time varied between individuals. When this random effect was added, along with the covariance between starting level and extent of linear change, the model deviance reduced significantly (Δ Deviance = 111 on 2df, p < 0.0005) and the unexplained within-participants variance was reduced by a further 4%. Of the demographic and control variables, only employment status and car had a significant effect upon Steps, with full-time and part-time employees likely to have a higher step-count than other groups and car owners likely to have lower step-counts than non-car owners.

The tests for hypotheses H1 and H2 show that Experimental Condition (a dummy variable coded with control group as the reference category) had a statistically significant effect (Individual vs. Control: B = 0.474, p < 0.05; Social norms vs. Control: B = 0.526, p < 0.05), explaining a further 7.7% of the between-participants variance in the step-count. Compared to those in the control condition, the average expected stepcount of those in the individual feedback condition was 59% higher and that for the social feedback condition was 69% higher (an average of 64% for the two experimental conditions). The null hypothesis for H1 was therefore rejected in favor of the finding that those receiving feedback had higher step-counts than those in the control condition. However, there was no significant difference in Steps between the two experimental conditions, so the null hypothesis for H2 could not be rejected.

There was no evidence that Experimental Condition had any effect on betweenparticipants variation in the rate of change, over time, in the step-count. The interaction of experimental condition and time point was not statistically significant and this interaction reduced the model deviance by just 1 on 1df (p > 0.05), explaining only 0.6% of the variation in slopes.

Figure 4 shows the temporal variation in step-counts and illustrates the findings presented above. Over the course of study, those in the two feedback conditions opened the app an average 3.9 times per day (median = 3.5, SD = 2.6), on each occasion keeping it open and visible on the screen for an average of 32.0 seconds (median = 33, SD = 9.0). Indeed, 87% of those in the individual condition and 89% of those in the social norms condition used it every day for half or more of the study days, and in the final week of the study participants from the two feedback conditions were still opening the app on average 2.3 times a day (SD = 1.9; median = 1.9). The survey evidence suggests that these figures reflect genuine enthusiasm for the app. Of those in the two feedback conditions 91% reported that the app was 'interesting', 67% that it was 'fun' and 73% that they would continue to use the app after the trial. Furthermore, only 19% reported losing interest in the app before the study end, only 15% of participants from the two feedback conditions reported that the step-count had not been "accurate enough for my needs" and only 11% that "lack of accuracy caused me to use the app less". The absence of any evidence for a non-zero correlation between perceived accuracy and either Steps or App Use indicates that problems with accuracy had little effect on the impacts of the app on behaviour.

As with Steps, variability in App Use was highly clustered within individuals. As illustrated in Figure 5, the app was opened most often in the first few days of the study, with usage thereafter decliningfirst rapidly and then more gently (i.e. a curvilinear effect of study time-point was found to be statistically significant). This decrease over time varied between participants in both shape and rapidity, as evidenced by the fact that the addition of random effects of the time and time-squared terms (and the covariances between intercept and slope) increased the goodness of fit of the model (Δ Deviance = 1288 on 5df, p < 0.0005).

Fig. 5. The average daily frequency with which participants opened the bActive application on their phones

None of the participant-level control variables had a statistically significant impact on App Use. This is somewhat surprising, for men with children and those in em-ployment (especially full-time employment) might be expected to have less time for such activities. The of any such effect suggests that interest in the feedback was sufficient for participants to use it in spite of other, conflicting, calls on their time and attention. The null hypothesis for H3 could not be rejected, for no difference was found between the two experimental conditions with regard to level or change of use.

Interview Findings

The interviews suggest that the increase in Steps by those in the intervention condition was the result of a number of features of the app design. Two of these were of particular importance: 1) the graphical display of walking patterns in the Past Week and History screens and 2) the always-on design, which meant that the app measured steps continually without having to be switched on. These features made users more aware of incidental walking. Interview respondents reported that before using the app they tended not to be aware of walking that was incidental to the achievement of other activities (e.g. the walking involved in shopping or work). The app made such walking more apparent, prompting one interviewee to comment, "...walking more than I thought, yeah. [...it is] surprising how much walking you do; just little bits here and there, walking around the workshop." This, in turn, showed users that they could become more active without having to engage in entirely new physical activities, and that all they needed to do was include more incidental walking in their existing activities.

The interview data further suggests that simply being seen to measure walking, the app encouraged users to view it as an activity in its own right. Where previously, walking had been "just something you do because it's a natural thing", it now became a measurable exercise that was subject to target-setting. Measurement also encouraged walking by helping users assess what was achievable. Before using the app, some users had relatively little understanding of distances, and the prospect of, for example, a two-mile walk might have been daunting ("Phew! Two miles, I'll hop on a bus" -David). By showing people how many miles they walked in the course of an ordinary day, the app made the concept of 'a mile' more familiar and thus made it more feasible for them to walk a number of miles ("Actually I walked two miles the other day and it seemed like nothing; I can walk that" -David). Finally, measurement highlights the difference between days with lots of incidental walking and days that are less active. This encourages users to walk more so that they are not obliged to see themselves as "lazy" or as "dossing".

The interviews do not fully explain the absence of an incremental impact for the social norms feedback. On the one hand, they suggest that those receiving social feedback became competitive and walked more when they thought they could "win" or "beat the average". On the other hand, there is some evidence that those who were below the average, either or a particular day or more generally, were less likely to walk more because they felt there was no possibility of 'winning'.

Discussion and Conclusions

This study indicates that always-on, accelerometer-based smartphone apps can increase amongst males by around 64%. This degree of behaviour change could have very real benefits for the general health of users and the prevalence of obesity and chronic disease in the population.

bActive's always-on feature allowed it to measure the walking inherent in practices not initially considered by users as 'walking' or 'exercise' and highlighted the periods in which they were relatively inactive. This had a transformative effect on some users, motivating them to avoid inactivity, making walking more of an activity in its own right, giving users the confidence that they could walk longer distances than they had previously realized and helping them to see that they could increase their activity levels simply by changing the way they conducted their usual activities.

Furthermore, there participants found the app engaging and enjoyable to use; with the trend data in the Last Week and History screens, in particular, holding their interest. Although use of the app fell away quickly from its initial high, the rate of decline slowed rapidly; participants were still accessing the app almost four times a day by the end of the study, and many expressed an interest in continuing to use it.

These results were achieved without the provision of any program of support or instruction, for apart from the feedback displays, the only input received by participants was the weekly motivational text message. This distinguishes the bActive evaluation from many previous studies, including many of those in the meta-analysis of pedometer interventions amongst the general population by Kang et al [START_REF] Kang | Effect of Pedometer-Based Physical Activity Interventions: A Meta-Analysis[END_REF], the largest of which [START_REF] Jackson | Increasing Walking in College Students Using a Pedometer Intervention: Differences According to Body Mass Index[END_REF][START_REF] Winnet | Guide to Health: Nutrition and Physical Activity Outcomes of a Group-Randomized Trial of an Internet-Based Intervention in Churches[END_REF] included extensive additional programs of motivation or instruction.

The absence of any evidence in support of hypotheses H2 and H3 can be interpreted either as reflecting on the design of the social feedback or as evidence that 'social sharing' is not as important for the promotion of exercise as has previously been argued (e.g. [START_REF] Consolvo | Design Requirements for Technologies that Encourage Physical Activity[END_REF]). The evidence in the literature on this issue is surprisingly weak, with assertions sometimes being made with little apparent empirical support. However, the lack of clear evidence for the effectiveness of social sharing does not allow its importance to be dismissed. Users were told that the comparison group comprised other males of about the same age who lived in Bristol, but it is possible that the salience and effectiveness of the comparisons would have been greater had users been given control over the types of people included. In addition, social sharing might have been approached in an entirely different way such as, for example, by allowing users to see the progress of other individual members of the comparison group.

A suggestion for future studies might be to increase the salience of the comparison group by defining it more tightly. Practitioners of the social norms approach generally argue that the most effective reference group comprises those whom participants consider most like themselvese.g. [START_REF] Berkowitz | The Social Norms Approach: Theory, Research, and Annotated Bibliography[END_REF][START_REF] Lewis | Gender-Specific Misperceptions of College Student Drinking Norms[END_REF]. This might, for example, mean separating those in physically active occupations from those doing more sedentary work.

A final consideration is the design of the feedback display. It is possible that the display of the social norms data on the same graph as the individual feedback detracted from the impact of the latter and that this, and not any lack of impact of the social data itself, weakened the impact of the full version of the app. Given that the individ-ual feedback alone was associated with an increase in walking of 64%, it is clear that this should be an important element any feedback strategy. However, when the social norm is much higher, even on just one day of the week, than a person's own highest step-count, the curve for the person's own steps becomes flattened and the allimportant variations in his own performance are less obvious.

Minor issues with battery power and measurement accuracy notwithstanding, it is clear that when it comes to measuring and encouraging active lifestyles, the ubiquity of smartphones lends phone-based apps an important advantage over dedicated pedometer devices that require up-front commitment to increased exercise. Furthermore, the passive nature of data collection used by the bActive app encouraged use of the app and allowed users to collect data effortlessly and continually.

Those using the app recorded on average 64% more steps than those that did not. This highlights the power of the bActive approach and draws attention to the potential for the use of individual-level feedback that encourages people to reflect on the patterns in their own behaviour, identify opportunities for change and realise those opportunities. From the evidence in this study, it seems likely that an approach modelled on bActive could have a real positive impact on the health and fitness of the population.

Fig. 1 .

 1 Fig. 1. Left: bActive's Today screen, as seen by those in the social feedback condition. (Note: the bActive icon is visible in the top left corner). Right: an entry on the Android notification bar indicates that the app is running in the background.

Fig. 3 .

 3 Fig. 3. The initial bActive screen as seen by those in the individual condition (Left) and control condition (Top)

Fig. 4 .

 4 Fig. 4. Daily average step-counts for the three experimental conditions

Table 1 .

 1 Features of the interview sample

	Month of	Total Feedback		Partici-	Liv-	Self-	Still	Self-reported
	interview		condition:		pant age	ing	reported	using	comparison
			C-Control				with	change	app	to average 3
			I-Individual S-Social			chil-dren 1	in walk-ing 2	April 2012	A-above B-below C-varying
			C	I	S	20s 30s		+ 0 -		A B C
	Jan 2012	7	2	2	3	5	2	3	4 2 1	n/a	1 1 1
	Oct 2012	8	0	3	5	5	3	3	7 0 1	3	0 4 1
	1

see, for example, Nokia's Wellness Diary (http://betalabs.nokia.com/apps/wellness-diary)

http://www.mapmywalk.com/

a mechanism to prevent the device from entering a low-power state

http://developer.android.com/reference/android/os/PowerManager.html

Acknowledgements.

The authors would like to thank the Research Councils UK Digital Economy Programme for supporting the bActive study and the wider CHARM project of which it was a part. Thanks, too, to Mubaloo, Bristol, for their constructive technical comments on the bActive app.


Sidney Fels
email: ssfels@ece.ubc.ca

Junia Anacleto

Roberto Calderon
email: roberto@alumni.ubc.ca

Towards Supporting the Existing Workplace Practices of a Community of Brazilian Healthcare Professionals

Keywords: Visualization, Situated Displays, Healthcare, Collaboration, Social Capital

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

One of challenges regarding the introduction of Information Communication Technologies (ICT) into the workplace is understanding the effects of such technologies on the functioning of the communities that work there. In the present paper, we investigate the challenges of supporting the existing workplace practices within a community of healthcare professionals in a Brazilian centre for mental care.

In particular, we aim at documenting how a community that has had little or no previous exposure to digital Information and Communication Technologies adopts the introduction of digital ICT and how the functioning of such community is affected by the new technology. Through a previous ethnographic study [START_REF] Calderon | Understanding NUI-supported Nomadic Social Places in a Brazilian healthcare Facility[END_REF] of this community, we uncovered a communication system based on verbal communication and pen-and-paper records that is sustained by informal socialization carried within the workplace throughout the day. Interpersonal communication and impromptu gatherings allow for efficient flow of information, that allows for flexible hierarchical structures and efficient task and patient status tracking.

Previous research has uncovered that situated public displays [START_REF] Lasome | Large public display boards: a case study of an OR board and design implications[END_REF], and in particular the representation of ad-hoc work-flows [START_REF] Fouzi | A web application for supporting healthcare tasks with a groupware planning approach[END_REF][START_REF] Wilson | Not all sharing is equal: the impact of a large display on small group collaborative work[END_REF] can promote communication and enhance collaboration between healthcare professionals. However, the appropriation patterns and how such technology affects the social structure supporting collaboration within communities with low technical background is still unexplored. The present research focuses on determining the characteristics of ICT that could support the adhoc socialization practices that are essential to the community being studied.

Using a user-centred design we designed a ticketing system utilizing a situated public display and compared two types of visualizations: one depicting individual actions and a second one representing collaborative and group actions. We observed that the community of healthcare professionals modified the original purpose of the technology in order to accommodate unimplemented functionalities (e.g. capitalizing co-workers' names and adding task dates), and to fit their long-established information and communication practices (e.g. using the ticketing system to engage in conversation, and to publicize messages of sympathy and support towards coworkers).

Related Work

Systems to provide social awareness in order to promote collaboration within a workplace include the Notification Collage [START_REF] Greenberg | The notification collage: posting information to public and personal displays[END_REF], and IM here [START_REF] Huang | Im here: public instant messaging on large, shared displays for workgroup interactions[END_REF]. These projects build upon a long history of research [START_REF] Fitzpatrick | Augmenting the workaday world with elvin[END_REF][START_REF] Macintyre | Support for multitasking and background awareness using interactive peripheral displays[END_REF] on the effect of public displays to promote collaboration within co-workers. Yet, this research often takes place within an office or research lab environment, which have different social dynamics and work-flow patterns from their healthcare counterparts. The benefits of public displays to promote collaboration in healthcare settings have been previously identified by Lasome et al. [START_REF] Lasome | Large public display boards: a case study of an OR board and design implications[END_REF], who argue that public displays, like task boards, are naturally used to provide increased task management and shared awareness leading to more efficient problem solving. Within a healthcare setting, some researchers [START_REF] Fouzi | A web application for supporting healthcare tasks with a groupware planning approach[END_REF][START_REF] Wilson | Not all sharing is equal: the impact of a large display on small group collaborative work[END_REF] have investigated the use of task management systems to promote collaboration. This body of work suggests that the introduction of technology within the healthcare workplace changes how information is shared within members of the workplace, often modifying how interpersonal connections are made. Such investigations argue that the introduction of technology, particularly public displays to track and publicize workplace tasks, has many benefits such as an increase on collaboration [START_REF] Bardram | Awaremedia: a shared interactive display supporting social, temporal, and spatial awareness in surgery[END_REF] and socialization [START_REF] Kim | Design and implementation of the location-based personalized social media service[END_REF] patterns. The need to document the differences between HCI in the developed world and HCI for the developing one have been previously stressed in the literature [START_REF] Chetty | HCI4D: hci challenges in the global south[END_REF]. Similarly, the need to investigate the role of ICTs for healthcare communities with little exposure to technology has been previously stressed in the literature [START_REF] Ho | Communication and computing in health facilities of southwest Uganda[END_REF].

CAIS Hospital for Mental Care

CAIS Clemente Ferreira (the hospital) located in Lins, São Paulo, Brazil, is a nonprofit state-run institution recognized as a Brazilian centre of excellence in mental health focusing on psychiatric disorders and neurological damages. The hospital currently employs over 380 professionals and provides care to more than 800 patients into eight care units serving different types of mental disorders and age groups. The architectural setting of the hospital -isolated urban location, corridors exceeding 100 meters in length and thick walls built in the early 1900s -make the installation of information and communication technologies a challenge, that paired with low governmental budgets have fostered a technology-free environment. In addition, many of the healthcare professionals have not been directly exposed to computing in their daily lives, as personal computers are rare and expensive in the area. As a result, healthcare professionals working at the hospital have developed, over the course of 25 years, an informational system based on pen-and-paper and verbal communication.

The community studied is formed of auxiliary nurses, registered nurses, doctors, physiologists, phonologists and administration personnel. Following an ethnographic study we discovered that this community leverages socializing practices (e.g. storytelling) to communicate and collaborate.

Social work-flows currently adopted by the community

In order to identify the natural practices adopted by the community being observed, we performed an ethnographic study where we shadowed members of the healthcare community [START_REF] Calderon | Understanding NUI-supported Nomadic Social Places in a Brazilian healthcare Facility[END_REF]. In this study we identified the existence of informal gatherings and the use of storytelling as the core factors supporting collaboration between members of this community. Informal gatherings taking place on corridors, nursing rooms and hidden corners serve to promote verbal communication where tasks are informally delegated or tracked through playful conversations. This promotes an on going process of storytelling involving both personal and work-related stories that serve to efficiently track the status of important tasks, e.g. a new patient arrival. In this process of storytelling, professionals of all ranks are socially expected to participate and engage into vivid and friendly conversations. As a result, the otherwise inflexible job hierarchies and descriptions become flexible. That is, often people go off their job description and daily tasks to resolve issues discussed during these social meetings. We found that these naturally adopted collaboration practices are enhanced by a culturally rooted understanding of community where verbal and playful sociability is celebrated and exercised on every interpersonal encounter.

Design of a Task Managing System to Support Informal Gatherings and Storytelling.

Our ethnographic study uncovered that daily tasks within the hospital are managed through informal gatherings and storytelling. In order to understand how ICTs can support these practices we designed a task managing application and compared two visualizations. A task managing kiosk, allowing healthcare professionals to manage tasks using a keyboard and mouse (Fig. 1), was designed following a user-centric iterative process. Two graphical representations of were made public for one month each, in order to measure the effects of different types of data visualization on the community. The first visualization (individual-centric) was designed to represent the individual count of resolved tasks. The second visualization (community-centric) was designed to represent individual actions in relationship to the community. The first visualization (Fig. 2-A) consisted of circles representing each of the professionals of the wing. The radius of each circle was computed as the number of issues resolved by a person Ri. The positioning of each circle was randomized at screen initialization. Understanding this visualization was straightforward, as people resolved more tasks, a circle with their name increased in size.

The second visualization (Fig. 2-B) focused on representing the relationship of each person to other members of the group and the group as a whole. Each person was represented as a circle whose radius was computed as the ratio of issues resolved by a person Ri to all issues resolved by all the members of the group R, i.e. Ri/R. Circles were then arranged equidistantly from each other in a circle, and lines were drawn to represent links created between the person that created an issue and the person that resolved it. The width of such lines was computed as the ratio of issues resolved by a person i and issued by person j , Ri→j, to all issues resolved by person i, that is Ri→j/Ri.

The size of circles represented people's ranking, rather than their number of tasks resolved. Therefore, changes in size would only be apparent if people solved considerably more tasks than their similarly-ranked peers. Additionally, thicker lines often meant that people would interact between each other more often. To make this clearer to the users, a legend was included on the bottom right part of the visualization explaining that larger circles "resolved more" and smaller circles "resolved less", while thinner lines depicted "less cooperation" and thicker lines represented "more cooperation".

Methodology Used for Semantic Analysis of Tasks

In order to understand the motivations driving the usage of the kiosk we performed a semantic analysis of all tasks submitted through our prototype. Our analysis was theoretically grounded on the Interactive Places Framework proposed by Memarovic et al. [START_REF] Memarovic | The interacting places framework: conceptualizing public display applications that promote community interaction and place awareness[END_REF]. The Interactive Places Framework proposes understanding content produced and consumed through public displays according to three strands: information production, means of disseminating such information and awareness of such information. Memarovic et al.'s conceptual framework models public display content as information created and consumed by either individuals or groups of people (i.e. with different levels of exclusivity) that when made available through the public display can be either explicit or implicit to a particular group of people or an individual (i.e. promoting different types of awareness).

Exclusivity

When content is directed towards anybody that comes in contact with the public display said content is said to be inclusive, however, when it is limited to a particular individual or group is said to be exclusive. When semantically analysing tasks submitted to our ticketing system we characterized exclusive content as tasks directed towards a particular healthcare professional or a group (e.g. "[MARTHA]: please fix the lamp on the bathroom" or "NIGHT TEAM: don't forget to stamp your forms after daily check-ups."). We characterized inclusive content as generalized tasks not directed towards a particular person or a group of people, but to the whole community of healthcare professionals (e.g. "Patient [X] will not be receiving visitors this weekend.").

5.2

Awareness

Although a public display might be broadcasting information in a public manner not all people that come in contact with the content are able to make sense of it, or find it relevant to their interests and values. Explicit content portrays information about the interests and values of particular individuals or groups of people within the community. Implicit content is generalized information not pertaining a particular individual, or interest within the community. When semantically analysing tasks we considered explicit messages to be exemplified by calls for action from either particular individuals or the community (e.g. "[MARTHA]: please fix the lamp on the bathroom" or "TEAM don't forget to stamp your reports"). By contrast, tasks conveying generalized states of the workplace or unaddressed messages of conviviality were considered as implicit (e.g. "TEAM Have a good weekend" or "Emergency room light stopped working").

Findings

We observed that professionals modified the original intended usage of the system to accommodate their existing practices. The original design of the ticketing system did not allow assigning tasks to particular individuals or groups. However, shortly after deployment some users begun addressing individuals by commencing new tasks with capitalized names (e.g. "[MARTHA] please fix..."). By the third week, this was considered a regular way to begin a message addressed to a particular individual or a group of people. Similarly, professionals created a way to determine task timings and deadlines by adding a date at the end of the submitted tasks. (e.g. " [MARTHA] attend Occupational Health day 15/7 8:30"). In both scenarios repetitive usage led to a common practice adopted by all members of the community of healthcare professionals.

We found that although the system was originally designed as a ticketing system to publicize tasks regarding the workplace healthcare professionals used it to post nontask messages to other professionals (e.g. "[Doctor X] welcome back from your vacation"), encouraging messages (e.g. "May we find strength to do our jobs today") and open ended socialization phrases (e.g. "Good day" or "Good weekend everyone"). In some cases we found traces of conversational posts (e.g. "I thank everyone for the support and help. May god bless you. Kisses." to which somebody replied "We are with you [Martha]" ).

Semantic analysis findings

We observed that during the individual-centric visualization the majority of tasks submitted were often explicitly directed towards a particular goal, person, or group of people (e.g. "Please stamp the examination request from [JACK]..." and "[MARTHA] please take care of my baby"). The majority of tasks submitted during this visualization often called for specific actions from members of the community.

By contrast, during the deployment of the community-centric visualization the number of implicit tasks depicting a status of the workplace (e.g. "[MARTHA],

[GUS]'s wheel chair is wet") or messages to promote general awareness of a future or past event (e.g. "TEAM... [JACK] will go on the outing to the zoo day 04.12.2012...") greatly increased.

We observed that during the individual-centric visualization the majority of task were exclusive, addressing particular healthcare professionals or groups of people, and often contained the capitalized name of the addressee in the body of the task. However, when the community-centric visualization was deployed the number of inclusive tasks, i.e. tasks not addressed to a particular individual and concerning anybody that came in contact with the display, surpassed that of exclusive, i.e. directed, tasks.

Conclusion and Future Work

The present study investigates how ICTs can help support the existing practices of a community of healthcare professionals in a Brazilian centre for mental care. We performed an ethnographic investigation of this community and uncovered that informal gatherings and storytelling are essential to the centre. These practices promote efficient task delegation and tracking, and a sense of community that has as benefit a better functioning workplace.

A ticketing system using a public kiosk was designed using a user-centred design process to support task tracking and two graphic representations of the data collected were compared. We found that a visualization depicting individual actions promoted tasks demanding specific actions from particular individuals or groups of people. By contrast, we found that a community-oriented visualization enticed healthcare professionals to publicize tasks that were open-ended and usable by all members of the community using the system.

Our future work aims at understanding the role that community reciprocity and common interests play in adopting digital information and communication technologies. We are applying the lessons learned in the above study to design a system to support open-ended and social notices that can be used by all the community.

Fig. 1 .

 1 Fig. 1. Healthcare professionals use the public task-managing kiosk.

Fig. 2 .

 2 Fig. 2. Left: Individual-centric visualization showing people as isolated circles. (Screen-shot taken with all tasks collected during all 8 weeks.). Right: Community-centric visualization showing circles whose radius and connections depict people in relationship to the community or other people. (Screen-shot taken with all tasks collected during all 8 weeks.)

Acknowledgements

We thank our funding agencies: GRAND, FAPESP, CAPES/DFAIT for their support. We thank the CAIS Hospital staff, MAGIC and our colleague, Jônatas Leite de Oliveira, for his help and insight with this project. The research was performed under the CAIS Ethical Statement.