Abstract
Self-monitoring is the cornerstone of many health and wellness persuasive interventions. However, applications designed to promote health and wellness that use this strategy have recorded varying degrees of success. In this study, we investigated why the self-monitoring strategy might work in some contexts and fail in others. We conducted a series of large-scale studies, with a total of 1768 participants, to explore the strengths and weaknesses of the self-monitoring strategy. Our results uncover important strengths and weaknesses that could facilitate or hinder the effectiveness of self-monitoring to promote the health and wellness of its users. The strengths include its tendency to reveal problem behaviours, provide real and concrete information, foster reflection, make people accept responsibility, create awareness and raise users’ consciousness about their health and wellness. Some of the weaknesses include its tendency to provoke health disorder, be tedious and boring. We contribute to the digital health community by offering design guidelines for operationalising self-monitoring to overcome its weaknesses and amplify its strengths.
Keywords: Persuasive technology, self-monitoring, behaviour tracking, persuasive strategy, wellbeing, persuasion, health, wellness, strengths, weaknesses, captology
Introduction
The use of persuasive technologies (PTs) aimed at bringing about desirable change by shaping and reinforcing behaviour, attitude or both, is growing in virtually all areas of health and wellness. Persuasive strategies – techniques employed in PT designs to promote desirable behaviour – are the cornerstone of PTs. Self-monitoring is the most widely employed strategy in interventions aimed at promoting health and wellness.1 It allows people to track their own behaviour by providing information on both their past and current states.2,3 Self-monitoring often involves self-evaluation, periodic measurement and recording of the target behaviour by the user.4
Self-monitoring has been used in many persuasive interventions aimed at motivating behavioural change in various domains of health and wellness. For example, it has been employed in PTs for promoting physical activity,5 weight management,4,6 healthy eating7 and in the area of substance abuse prevention such as smoking cessation.8 Despite its wide application, researchers have reported inconsistencies in its effectiveness4: there are varying degrees of success, mixed findings, and even failures.4,9,10 There is no research that explains the reason behind the aforementioned variations in the effectiveness of self-monitoring. This is essential for effective operationalization of the self-monitoring strategy in PT intervention design and hence advancement of the field of PT.
To contribute to research in this area, we conducted two large-scale studies involving 1108 and 660 (a total of 1768) participants to explore the strengths and weaknesses of the self-monitoring strategy. We investigated the strategy in the context of PT interventions for promoting healthy eating behaviour (study one) and PTs for motivating change in risky health behaviour such as binge drinking (study two). Investigating two distinct health domains allowed us to uncover a wide range of strengths and weaknesses that could be generalised to other domains. We used the prototype persuasive implementation of the self-monitoring strategy, which has been validated in other studies.2,11 Our results reveal important characteristics of the self-monitoring strategy that could facilitate or hinder its effectiveness in promoting health and wellness. The strengths include its tendency to: (1) reveal problem behaviour, (2) provide real and concrete information, (3) foster reflection and make people assume responsibility for their behaviour, (4) create awareness and raise users’ consciousness about their health and wellness. Some of the weaknesses include its tendencies to: (1) provoke health disorders, (2) become tedious and (3) be boring. The manner in which the self-monitoring strategy is operationalised in a PT can amplify both its strengths and weaknesses.
We offer two main contributions that would advance the field of digital health and persuasive design. First, we reveal the strengths and weaknesses of the self-monitoring strategy – the most frequently employed strategy in the area of health and wellness – that should be taken into account by PT designers when employing the strategy. Second, based on our findings, we offer design guidelines on how to implement the self-monitoring strategy in PT design to amplify their strengths and overcome their weaknesses.
Related work
In this section, we present a brief overview of the self-monitoring persuasive strategy.
Persuasive strategies and their applications in PTs
Persuasion is often achieved in PT design using various persuasive strategies.12 Over the years, PT researchers (such as Fogg, and Oninas-Kukkonen) have developed a number of persuasive strategies.13,14 According to a recent meta-analysis of persuasive technologies,1 self-monitoring is the most commonly employed strategy in PT interventions aimed at promoting health and well-being. Self-monitoring often involves self-evaluation, periodic measurement, and recording of the target behaviour by the user,4 thereby allowing people to track their own behaviour by providing information on both their past and current states.2
Persuasion has been widely employed in nutrition monitoring interventions to motivate people to adhere to their dietary regime.7 It has also been used in the area of weight management for consistent monitoring of weight to encourage weight loss.4,6 It is the most common strategy used in interventions for promoting physical activity, for example UbiFit,5 Houston15 and Fish ‘n’ Step,16 and in preventing substance abuse such as smoking cessation.8 Self-monitoring has also found application in the interventions for managing chronic illnesses such as diabetes,17 cancer18 and heart disease.19 For a detailed review of persuasive health interventions and strategies employed, see Orji and Moffatt.20
Although the self-monitoring strategy has been applied widely across several health and wellness domains to promote behaviour changes due to its perceived effectiveness, researchers have recorded mixing findings and even failures.4,9 There is thus far no research into why PT interventions that employ self-monitoring to promote health and wellness, exhibit varying degrees of success and why it may work in one context and fail in another. Our study aims to fill this gap by exploring the strengths and weaknesses of the self-monitoring strategy.
Study Design and Methods
The purpose of this study was to explore the strengths and weaknesses of the self-monitoring strategy. To achieve this, we conducted two studies. The first focused on PTs for motivating healthy eating behaviour and the second focused on PTs for motivating a change of risky alcohol-consumption behaviour. To collect data for our studies, we used prototype persuasive implementation of the self-monitoring strategy that has been validated in other studies.2,11 We specifically represented the self-monitoring strategy in a storyboard about a persuasive intervention for encouraging healthy eating (study one) and a PT for promoting change of risky alcohol behaviour (study two). The storyboard was drawn by an artist and was based on storyboard design guidelines by Truong et al.21 Implementing the strategy in a storyboard made it easier to elicit responses from diverse populations because storyboards provide a common visual language that individuals from diverse backgrounds can read and understand.22 The implementations closely imitated how the strategy is usually operationalised in existing persuasive interventions from the literature.7,23
In study one, the storyboard showed a character and its interactions with a PT for motivating healthy eating behaviour and in study two, the storyboard showed a character and its interactions with a PT for promoting change of risky alcohol behaviour. We evaluated and iteratively refined the storyboards. Figure 1 shows an example of one of the storyboards illustrating the self-monitoring strategy in the healthy eating domain.
To elicit qualitative feedback about the strategy, we closely followed a well-established method that has been used in several human-computer interactions and persuasive PT papers.2,11,24–27 Specifically, the strategy was followed with an open-ended question that required the participants to comment on the strategy represented in the system and how they would use it. Additionally, the users were required to rate and justify their rating of the strategy with respect to the effectiveness – the strengths and weaknesses. Prior to evaluating the strategy, we gave our participants the following instructions ‘imagine that you are using the system presented in the storyboard above to track your daily eating (or alcohol use in study 2)…, please, answer the following questions.’ We ensured that the participants understood the strategy depicted in the storyboard by asking them to describe what is happening in the storyboard in their own words (‘In your own words, please describe what is happening in this storyboard’). We also included questions for assessing the participants’ demographic information and eating and drinking behaviour.
We recruited participants for this study using Amazon’s Mechanical Turk (AMT). After filtering out incomplete responses and incorrect responses to comprehension and attention-determining questions, a total of 1768 responses were included in our analysis (1108 responses from study one and 660 responses from study two). In the two studies, our participants were at least 18 years of age at the time of data collection and were capable of reading and understanding English well. In addition to this, for study two, participants were required to have consumed alcohol at some time. The participants received a small compensation for their time. We ended up having a relatively diverse population sample in the terms of gender, age and education level attained, as shown in Table 1.
Table 1.
Total number of participants = 1768 | |
---|---|
Gender | Females (49%), Males (51%). |
Age | 15‒25 (32%), 26‒35 (38%), 36‒45 (18%), Over 45 (12%). |
Formal Education | Less than high school (1%), High school (31%), College diploma (13%), Bachelor’s degree (37%), Master’s degree (15%), Doctorate (2%), Other (1%). |
Data Analysis and Results
To tease out the strengths and weaknesses of the self-monitoring strategy, we conducted a thematic analysis of 58 pages of qualitative comments about the self-monitoring strategy from our participants.28 The comments were analysed in an iterative manner to identify the central themes within them and their relationships and classify them into strengths and weaknesses until no further ideas emerged. The following is a representation of the key themes that transpired from the analysis.
Strengths and weaknesses of the self-monitoring strategy
In this section, we present the strengths and weaknesses of the self-monitoring strategy.
Strengths of the self-monitoring and feedback strategy
There were six major strengths of the self-monitoring strategy that made it effective at promoting health and well-being, see Table 2.
Weaknesses of the self-monitoring strategy
Participants highlighted four major weaknesses of the self-monitoring strategy that could hamper its ability to promote health and wellness, see Table 3.
Discussion
Our findings uncover six main strengths and four weaknesses of self-monitoring strategy, see Tables 2 and 3. Collectively, these findings account for the varying degrees of success in the persuasive interventions that employ the self-monitoring strategy. In this section, we discuss the strengths and weaknesses and offer design recommendations for operationalising the strategy in PT interventions to minimise and overcome its weaknesses and reinforce its strengths.
Table 2.
1. Self-monitoring provides opportunity for
self-awareness and raises people’s
consciousness about their health and wellness:
|
2. Self-monitoring guides people and helps them
take control of their health and well-being:
|
3. Self-monitoring helps people stay accountable
and exposes hidden behavioural determinants:
|
4. Self-monitoring provides useful information on how to achieve desired behavioural outcome: |
|
5. Self-monitoring engages the users, encourages
them to reflect on their behaviour and thus enables
them to make informed decisions:
|
6. It allows users to monitor their progress and
performance towards their health and wellness
goal
|
Table 3.
1. Self-monitoring could be tedious and hence
discourage users from using a PT intervention:
|
2. It is not fun:
|
3. Self-monitoring could lead to health disorders
such as eating disorder and consequently, that could lead to
mental problems such as depression:
|
4. Self-monitoring may need complementary strategies to truly
motivate users:
|
Strengths
Self-monitoring raises consciousness and makes people reflect on their behaviour
Our finding shows that self-monitoring promotes health and well-being by motivating reflective thinking about an individual’s behaviour. Reflective thinking about behaviour is a well-known approach for motivating and sustaining behaviour change in line with the transtheoretical model of change (TTM).29 Research has suggested that reflective approaches to behavioural changes have the potential to intrinsically motivate users, thereby resulting in long-term behavioural change.29,30 Self-monitoring raises users’ consciousness about their health behaviour. This is in line with consciousness raising and self-evaluation process of behavioural change identified by the TTM of change, which occurs when people learn more about their behaviour.31 Therefore, to raise users’ consciousness about their behaviour and motivate long-term behaviour change via reflection, designers should employ the self-monitoring strategy.
Self-monitoring makes people assume responsibility for their behaviour by revealing problem behaviour
Attribution theory explains how people tend to assign the causes of a behaviour to some situations outside their control rather than blame it on themselves – external attribution.32 There are many confounding factors that may contribute to ill-health and well-being (including lifestyle and genetic factors), some of which are beyond an individual’s control. As a result, some people tend to attribute their state of health and wellness to factors that are outside their control, such as genotype. Hence, they are unwilling to make any changes in their lifestyle that they may perceive as good enough. Self-monitoring could reveal problematic behaviours and make people assume responsibility of their health and wellness as opposed to attributing it to other factors beyond their control. As shown by the comment ‘I think it would be helpful. I always say I don't drink a lot but I never count to know. It improves self-awareness’ [P1591]. ‘I like being held accountable for my actions and this system would help with that certainly’ [P1]. ‘Tracking is always a great way to understand why things are not changing (such as no loss of weight)’ [P234]. Therefore, designers should operationalise self-monitoring to expose problematic behaviours to motivate those who are reluctant to change their behaviour, believing that their health and well-being are determined by factors that are not under their control.
Self-monitoring promotes intrapersonal competition
To shed light on the mechanism through which self-monitoring promotes health and wellness, many people highlighted that self-monitoring provides opportunity for them to compare their performance with their goal and their past performances. Therefore, it allows for an intrapersonal competition between their past and current behaviour as they tend to strive to break their past record. It was on that note that a participant made this comment: ‘Personal competition is somewhat sufficient and better than interpersonal competition when it comes to healthy living’ [P78]. Therefore, we recommend that designers should operationalise self-monitoring to allow users to compare their current and past behaviour and hence motivate behavioural change through intrapersonal competition. Designers could also reward users for outperforming themselves, thereby consciously provoking intrapersonal competition which could replace the conventional interpersonal competition strategy, especially for people who do not respond positively to competition with others and judgement.24
Weaknesses
Self-monitoring may lead to health disorder and depression
A shortcoming of self-monitoring, as pointed out by our participants, is that it may lead to health disorders. For example, an application focused on tracking calorie intake (for food apps) may provoke eating disorder in people with the tendency of being anorexic.
‘When I started my eating disorder behaviour, it began with a simple desire to be healthier (I was overweight), and it began with calorie counting daily. Then I began deciding to lower my caloric intake each day, pushing myself to do better/eat less. Less than a year of this behaviour landed me in the hospital with a damaged heart, damaged bones, and virtually no memory’ [P847].
To overcome this impediment, we suggest that designers should take a holistic approach to promoting health and wellness as opposed to tracking just one marker of health behaviour. For example, a healthy eating application that employs self-monitoring could also track how many serving of fruits and vegetable an individual consumed daily rather than focus on just the quantity of calories consumed. An application that discourages risky alcohol drinking could alongside tracking the quantity of alcohol, track the amount of water and food consumed. This is supported by the comment:
‘Counting calories feels oppressive and punishy. However, meeting other healthy eating goals (five fruits and veg) feels less judgmental. Not saying there should be no calorie tracking, but if that was the only way to get info, it would be a huge turnoff.’ [P1441].
This is also in line with other research that discovered that persuasive intervention could backfire if not strategically designed.33
Self-monitoring can be tedious
Another major drawback of many applications employing the self-monitoring strategy is the labour-intensive nature of current self-monitoring tools, which makes them tedious to use. Although technological advances, such as pedometers and arm-band sensors for physical activity monitoring, have allowed for automatic monitoring of certain behaviours, there are still some limitations on the type of behaviours that can be monitored automatically. For instance, not all kinds of food and drink intake can be monitored automatically.11 There are two main reasons why people perceive self-monitoring as tedious: the first has to do with the fact that self-tracking and recording one’s own behaviour is unnatural. As highlighted by Burke et al.,4 it is not natural for humans to track their own behaviours, thereby making it feel more like a punishment. This is also supported by a participant’s comment ‘Counting calories feels oppressive and punishy’. Along with this comes the added difficulty of figuring out the actual behaviour count as highlighted by the participant’s comment: ‘The hardest part of these systems is having to figure out the calories and enter them in’ [P118]. To overcome this limitation, we suggest that designers who employ self-monitoring should simplify the process and reduce the amount of work involved by automating behaviour monitoring process using tools such as pedometers and armband sensors for physical activity. However, we acknowledge that there are still some limitations with respect to what behaviours can be monitored without the user’s involvement. Therefore, for such behaviours that cannot be automatically monitored, such as some types of food and the amount of alcohol consumed, designers should incentivise users and reduce the perceived tediousness of the self-monitoring process using complementary persuasive strategies such as reminding users to log their behaviour, rewarding users for tracking their behaviours each day, and reducing the number of steps required to record behaviour. For example, a PT intervention that is designed to allow a user to select the alcohol level or food contents from a prerecorded list would be an easier alternative to typing the contents. This is supported by the comment ‘The system for input of data should be as smooth as possible. Ideally, I would like to see some way of easily tracking alcohol intake with a wearable device. Though the technology might not be there’ [P101]. ‘A method for alerting should be used alongside to make the person more conscious of achieving his target’ [P98].
Self-monitoring may not work for people in the pre-contemplation stage of behaviour change
While self-monitoring is a theoretically and empirically grounded strategy, it assumes that people are motivated to change and hence ready to self-track their behaviours. However, that is rarely the case as highlighted in this comment: ‘It seems like the desire to change my behaviour in this scenario would have to be internal as the system only seems to monitor whether or not I achieved a goal that I set’ [P111]. This suggests that similar to goal setting,34 applications employing self-monitoring may work only for people who are ready to change their behaviour: those that have the motivation to change their behaviour but have not developed plans for doing so. It may work better for individuals in the contemplation, action and preparation stages of the transtheoretical model,31 who have the intention but not yet the means to change.34 Therefore, we suggest that self-monitoring may not be an effective strategy for promoting behaviour change in people who lack the motivation to change.
Self-monitoring can be dull (not fun) and calls for a complementary strategy
Another reason why an application employing self-monitoring may not motivate and sustain user’s motivation is that it is perceived as ‘not fun’. ‘User tracking would be tedious and not fun without rewards’ [P315]. ‘This combined with the reward system would make an effective product’ [P479]. This comment follows closely from the previous one and suggests that self-monitoring needs a complementary strategy in order to be effective for some people. This is also in line with Burke et al. that self-monitoring was more effective when used alongside with social support.4 It also supports Etkin, finding that, while self-monitoring may motivate desired behaviour, it can simultaneously reduce the enjoyment associated with those behaviours.10 This is because tracking can make enjoyable activities feel more like work, hence reducing their enjoyment. ‘As a result, measurement can decrease continued engagement in the activity and subjective well-being’. Therefore, we recommend that designers should employ self-monitoring alongside other supporting strategies such as reward, social comparison, cooperation and competition to engage users, provide support and motivate behaviour change. These supporting strategies have been found to be compatible in PTs in previous research.35
Limitations and future work
Our findings are based on our participant’s opinions of persuasive intervention prototypes at one point in time and may differ from persuasive interventions used for a longer time. Therefore, as part of our future work, we plan to conduct a longitudinal study to assess our findings in actual persuasive interventions that will be used over an extended period. We will apply the guidelines we describe above when designing and evaluating the effectiveness of actual persuasive interventions; we also plan to assess our findings across other health behaviour domains (e.g. physical activity, smoking and sleep) to investigate potential variations in effectiveness.
Conclusion
This paper explores why health and wellness applications that employ the self-monitoring strategy experience varying degrees of success. By investigating the strengths and weaknesses of the self-monitoring strategy via two large-scale studies with a total of 1768 participants, we found that the self-monitoring strategy possesses six major strengths that emphasize the mechanisms through which it promotes health and wellness. Self-monitoring is also associated with four weaknesses that explain why it may not be effective for motivating health and wellness for some people. The strengths include that self-monitoring raises people’s consciousness and makes them reflect on their behaviours, reveals problematic behaviour, promote intrapersonal competition and helps people assume responsibility for their behaviours. On the other hand, relevant weaknesses include the tendency of self-monitoring to lead to health disorders, and to become tedious and boring. Based on our findings, we offer design recommendations for implementing the self-monitoring strategy on health and wellness interventions to overcome its weaknesses and amplify its strengths.
Note
Quotes from participants are included verbatim throughout the paper, including spelling and grammatical mistakes. Emphasis has been added by the current authors.
Contributions
The first author designed and conducted the study. All authors contributed in preparing the manuscript.
Declaration of Conflicting Interests
The authors declare that there are no conflicting interests.
Ethical approval
All studies complied with the research ethics guidelines provided by the University of Saskatchewan and University of Waterloo, Canada.
Funding
The authors are grateful to the Canadian Government for funding the first author's research through Natural Sciences and Engineering Research Council of Canada (NSERC) and Banting fellowship.
Guarantor
Rita Orji, Faculty of Computer Science Dalhousie University, Canada.
Peer review
This manuscript was reviewed by two individuals, the authors have elected for these individuals to remain anonymous. Many thanks to anonymous reviewers and our study participants.
References
- 1.Orji R, Moffatt K. Persuasive technology for health and wellness: State-of-the-art and emerging trends. Health Informatics J 2016; 1:7–9. doi: 10.1177/1460458216650979 [DOI] [PubMed] [Google Scholar]
- 2.Orji R, Nacke LE, DiMarco C. Towards personality-driven persuasive health games and gamified systems. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, 6–11 May 2017, pp. 1015–1027. New York: ACM.
- 3.Rapp A, Cena F. Self-monitoring and technology: Challenges and open issues in personal informatics. In: Stephanidis C and Antona M (eds) Universal Access in Human-Computer Interaction. Design for All and Accessibility Practice UAHCI 2014. Lecture Notes in Computer Science, vol 8516. Cham: Springer, 2014, pp. 613–622.
- 4.Burke LE, Swigart V, Warziski Turk M, et al. Experiences of self-monitoring: successes and struggles during treatment for weight loss. Qual Health Res 2009; 19: 815–828. doi: 10.1177/1049732309335395 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Consolvo S, McDonald D, Toscos T, et al. Activity sensing in the wild: a field trial of ubifit garden. In: CHI ′08 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy, 5–10 April 2008, pp. 1797–1806. New York: ACM.
- 6.Butryn ML, Phelan S, Hill JO, et al. Consistent self-monitoring of weight: a key component of successful weight loss maintenance. Obesity 2007; 15: 3091–3096. doi: 10.1038/oby.2007.368 [DOI] [PubMed] [Google Scholar]
- 7.Connelly K, Siek KA, Chaudry B, et al. An offline mobile nutrition monitoring intervention for varying-literacy patients receiving hemodialysis: a pilot study examining usage and usability. J Am Med Informatics Assoc 2012; 19: 705–712. doi: 10.1136/amiajnl-2011-000732 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.McFall RM. Effects of self-monitoring on normal smoking behavior. J Consult Clin Psychol 1970; 35: 135–142. doi: 10.1037/h0030087 [DOI] [PubMed] [Google Scholar]
- 9.Marshall J, Robson J, Pring T, et al. Why does monitoring fail in jargon aphasia? Comprehension, judgment, and therapy evidence. BRAIN Lang 1998; 63: 79–107. [DOI] [PubMed] [Google Scholar]
- 10.Etkin J. The hidden cost of personal quantification. J Consum Res 2016; 42: 967–984. doi: 10.1093/jcr/ucv095 [Google Scholar]
- 11.Orji R, Vassileva J, Mandryk RL. Modeling the efficacy of persuasive strategies for different gamer types in serious games for health. User Model User-adapt Interact 2014; 24: 453–498. doi: 10.1007/s11257-014-9149-8 [Google Scholar]
- 12.Orji R, Mandryk RL, Vassileva J. Improving the efficacy of games for change using personalization models. ACM Trans Comput Interact 2017; 24: 32. doi: 10.1145/3119929 [Google Scholar]
- 13.Fogg BJ. Persuasive technology: Using computers to change what we think and do. Amsterdam: Morgan Kauffman, 2003. doi: 10.1145/764008.763957 [Google Scholar]
- 14.Oinas-kukkonen H, Harjumaa M. Persuasive systems design: key issues, process model, and system features persuasive systems design: Key issues, process model, and system features. Commun Assoc Inf Syst 2009; 24: 28. [Google Scholar]
- 15.Consolvo S, Klasnja P, McDonald DW, et al. Goal-setting considerations for persuasive technologies that encourage physical activity. In: Proceedings of the 4th International Conference on Persuasive Technology, 2009, pp 1–8, ACM: Claremont, CA.
- 16.Lin JJ, Mamykina L, Lindtner S, et al. Fish’n’Steps: Encouraging physical activity with an interactive computer game In: Dourish P, Friday A. (Eds.): Ubicomp 2006, LNCS 4206. Berlin: Springer, 2006, pp. 261–278. doi: 10.1007/11853565_16 [Google Scholar]
- 17.Polonsky WH, Fisher L, Schikman CH, et al. Structured self-monitoring of blood glucose significantly reduces A1C levels in poorly controlled, noninsulin-treated type 2 diabetes: results from the Structured Testing Program study. Diabetes Care 2011; 34: 262–267. doi: 10.2337/dc10-1732 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Zhang J, Liang Y-C, Lin X, et al. Self-monitoring and self-delivery of photosensitizer-doped nanoparticles for highly effective combination cancer therapy in vitro and in vivo. ACS Nano 2015; 9: 9741–9756. doi: 10.1021/acsnano.5b02513 [DOI] [PubMed] [Google Scholar]
- 19.Halm MA, Penque S. Heart failure in women. Prog Cardiovasc Nurs 2000; 15: 121–133. doi: 10.1111/j.0889-7204.2000.080399.x [DOI] [PubMed] [Google Scholar]
- 20.Orji R, Moffatt K. Persuasive technology for health and wellness: State-of-the-art and emerging trends. Health Informatics J 2018; 24: 66–91. doi: 10.1177/1460458216650979 [DOI] [PubMed] [Google Scholar]
- 21.Truong KN, Hayes GR, Abowd GD. Storyboarding: An empirical determination of best practices and effective guidelines. In: Proceedings of the 6th conference on Designing Interactive systems, 2006, New York: ACM, pp. 12–21.
- 22.Lelie C. The value of storyboards in the product design process. Pers Ubiquitous Comput 2005; 10: 159–162. doi: 10.1007/s00779-005-0026-7 [Google Scholar]
- 23.Tsai CC, Lee G, Raab F, et al. Usability and feasibility of PmEB: A mobile phone application for monitoring real time caloric balance. Mob Networks Appl 2007; 12: 173–184. doi: 10.1007/s11036-007-0014-4 [Google Scholar]
- 24.Orji R. Why are persuasive strategies effective? Exploring the strengths and weaknesses of socially-oriented persuasive strategies. In: de Vries PW et al. (eds) Proceedings of the 12th International Conference, PERSUASIVE 2017, Amsterdam, The Netherlands, April 4–6, 2017, Berlin: Springer, pp 253–266.
- 25.Busch M, Mattheiss E, Reisinger M, et al. More than sex: The role of femininity and masculinity in the design of personalized persuasive games. In: PERSUASIVE. Proceedings of the 11th International Conference on Persuasive Technology, Salzburg, Austria, 5–7 April 2016, Berlin: Springer, pp. 219–229.
- 26.Anagnostopoulou E, Magoutas B, Bothos E, et al. Exploring the links between persuasion, personality and mobility types in personalized mobility applications. In: de Vries PW et al. (eds) Proceedings of the 12th International Conference, PERSUASIVE 2017, Amsterdam, The Netherlands, April 4–6, 2017, Berlin: Springer, pp 107–118.
- 27.Orji R. Persuasion and culture: Individualism – Collectivism and Susceptibility to Influence Strategies. In: PERSUASIVE. Proceedings of the 11th International Conference on Persuasive Technology, Salzburg, Austria, 5–7 April 2016, Berlin: Springer, pp. 30–39.
- 28.Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006; 3: 77–101. doi: 10.1191/1478088706qp063oa [Google Scholar]
- 29.Orji R, Vassileva J, Mandryk RL. LunchTime: a slow-casual game for long-term dietary behavior change. Pers Ubiquitous Comput 2013; 17: 1211–1221. doi: 10.1007/s00779-012-0590-6 [Google Scholar]
- 30.Orji R, Mandryk RL and Vassileva J. Gender and persuasive technology: Examining the persuasiveness of persuasive strategies by gender groups. Persu Techno 2014; 48–52.
- 31.Prochaska JO, DiClemente CC, Norcross JC. In search of how people change: Applications to additive behaviors. Am Psychol 1992; 47: 1102–1114. [DOI] [PubMed] [Google Scholar]
- 32.Kelley HH. Attribution theory in social psychology. Nebraska Symp Motiv 1967; 15: 192–238. [Google Scholar]
- 33.Stibe A, Cugelman B. Persuasive backfiring: When behavior change interventions trigger unintended negative outcomes. In: Meschtscherjakov A, De Ruyter B, Fuchsberger V, et al. (eds) Persuasive Technology. PERSUASIVE 2016. Lecture Notes in Computer Science, vol 9638. Cham: Springer, 2016; 65–77. doi: 10.1007/978-3-319-31510-2
- 34.Karapanos E. Designing for different stages in behavior change. In: Proceedings of the workshop ‘Personalization in Persuasive Technology’, Persuasive Technology 2016, Salzburg, Austria, pp. 57–59
- 35.Oyibo K, Orji R, Vassileva J. Investigation of the social predictors of competitive behavior and the moderating effect of culture. In: Proceedings of ACM UMAP conference, Bratislava, Slovakia, July 2017 (UMAP’17), 2017. UMAP 2017, doi: 10.1145/3099023.3099113