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Article

Uplifting Moods: Augmented Reality-Based Gamified Mood Intervention App with Attention Bias Modification

by
Yun Jung Yeh
1,†,
Sarah S. Jo
2,† and
Youngjun Cho
1,*
1
Department of Computer Science, University College London, London, UK
2
RRTAI, Dublin, Ireland
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Software 2025, 4(2), 8; https://doi.org/10.3390/software4020008
Submission received: 22 December 2024 / Revised: 8 March 2025 / Accepted: 20 March 2025 / Published: 1 April 2025
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Abstract

:
Attention Bias Modification (ABM) is a cost-effective mood intervention that has the potential to be used in daily settings beyond clinical environments. However, its interactivity and user engagement are known to be limited and underexplored. Here, we propose Uplifting Moods, a novel mood intervention app that combines gamified ABM and augmented reality (AR) to address the limitation associated with the repetitive nature of ABM. By harnessing the benefits of mobile AR’s low-cost, portable, and accessible characteristics, this approach is to help users easily take part in ABM, positively shifting one’s emotions. We conducted a mixed methods study with 24 participants, which involves a controlled experiment with Self-Assessment Manikin as its primary measure and a semi-structured interview. Our analysis reports that the approach uniquely adds fun, exploring, and challenging features, helping improve engagement and feeling more cheerful and less under control. It also highlights the importance of personalization and consideration of gaming style, music preference, and socialization in designing a daily AR ABM game as an effective mental wellbeing intervention.

1. Introduction

It is estimated that more than 200 million people around the world are affected by anxiety disorder [1]. In addition, up to one-third of the population are affected by anxiety disorder during their lifetime [2]. Over the COVID-19 pandemic, there was an alarming increase of anxiety and depression across the world [3,4]. However, despite the growing demand of mental health care, the accessibility and acceptability of treatment options has been limited, hampering effective provision as only a small number of people receive adequate and effective intervention [5]. Problems can be found in two current major mood interventions: medication is likely to cause side-effects, and cognitive behavioural therapy (CBT) is relatively costly and time-consuming [6,7].
To address such limitations, online and mobile-based mental wellbeing interventions have been actively investigated as possible alternatives given their good accessibility and portability [8,9]. Among promising methods is Attention Bias Modification (ABM), an emerging low-cost, computer-based attention retraining protocol. ABM training engages a modified visual probe task with the goal of affecting emotions and reducing anxiety [10]. It is found to be effective in reducing anxiety levels by targeting attentional threat bias (a key cognitive mechanism in anxiety) through repeatedly attending to specific target positive stimuli and ignoring others [7,11] and may have the potential to be used in daily settings beyond the clinical environment.
ABM training is considered repetitive and not engaging, which may reduce treatment compliance [12]. Combining gamification techniques to address the core issues of diminishing motivation to ABM treatments [13], researchers have recently reported that gamified interventions can improve the performance of the attention bias modification task [14,15,16]. This has a greater potential for promoting engagement in ABM treatment for managing stress, anxiety, and substance use, receiving considerable interest from the health research community [17,18]. Personal Zen is an exemplary application [12,15,19], showing positive effects on a reduction of threat bias, subjective anxiety, and stress reactivity with physiological evidence (cortisol level dropping compared with placebo training). It has also been shown that gamified ABM has wider mental wellbeing intervention benefits, such as reducing attention dwelling on threats [20], intervening excessive alcohol consumption [14], and enhancing optimism bias [21]. However, the gamification approach only has not always been effective, demonstrating some incongruent results and reports of limited motivation [22,23,24,25,26].
Augmented reality (AR) has received growing attention when it comes to offering immersive experiences [27,28,29]. AR enhances real-world interactions [30,31]. With its highly immersive nature, AR is not only a popular choice in gaming and educational contexts, but also has been found to be effective in the health care domain, for example in surgical training [32]. In clinical psychology, researchers have demonstrated that applying AR in cognitive therapy can successfully help reduce anxiety, avoidance behaviours, and substance addiction [33,34,35]. For example, Botella et al. found that the AR treatments produced a decrease in patients’ fear and avoidance behaviours [36,37]. Furthermore, applying AR to mobile applications has been found to be cost-effective, portable, and to have more accessibility features in comparison with headset-based AR [31]. The wider benefits of mobile AR intervention were highlighted in treating anxiety disorders [28] and supporting people with ASD (Autistic Spectrum Disorders) [31] and children with ADHD (Attention Deficit Hyperactivity Disorder) [38]. However, this compelling interactive feature has not been explored and considered in ABM intervention, which can possibly be applied to a wide range of real-life situations.
Thus, in this paper, we aim to investigate mobile AR integrated into a gamified ABM and its effects in uplifting a user’s mood states. We develop Uplifting Moods, a mobile AR ABM gamified mood intervention app, based upon a conceptual framework illustrated in Figure 1. Our proposed intervention schema suggests that AR enhances ABM by increasing user immersion, while gamification adds motivational supports that promote sustained engagement. These combined effects are expected to enhance emotional processing and mood regulation, leading to improved outcomes in ABM. Our evaluation, involving a mixed study with 24 participants, is guided by our two research questions: To what extent is this new approach effective in improving people’s moods across two major affective dimensions: arousal and valence? And what features are important for designing an engaging and effective ABM game that keeps motivating users? Our findings inform design implications for researchers, software developers, and designers to build joyous ABM games as a motivative intervention to help enhance people’s mood in real-life situational contexts.
Contributions—this paper makes three contributions: (i) Uplifting Moods: Design and implementation of a novel mobile intervention app combining AR and ABM gamification for improving daily mental wellbeing; (ii) Report on studies that demonstrate the effectiveness of our proposed AR-enabled ABM through a series of quantitative and qualitative data collections and analyses; and (iii) Implications for software developers, designers, and practitioners in developing mental health intervention software with AR-enabled ABM gamification that can be more engaging and effective.

2. Materials and Methods

2.1. Gamified Intervention with AR and ABM: Uplifting Moods

As shown in Figure 2, we have developed a mobile application, Uplifting Moods, and investigated the effectiveness of combining AR and ABM treatment in eliciting beneficial emotional outcomes. Two independent variables (IV)—presence of ABM and presence of AR (with each having two conditions)—are set out for this experiment, as summarised in Table 1. The first IV is ABM with two conditions: ABM and non-ABM. The first condition focuses on positive and negative stimuli referred to happy and angry imaginary animal characters (lively dragons), where participants are asked to search for the characters with positive emotions. In a gamification context, the search task is referred to as “petting happy dragons (characters with positive emotion)”. By contrast, the second condition asks participants to follow neutral objects without emotional expressions (“collecting food” in gamification). The second IV conditions are AR and non-AR, which decides whether to present both ABM and non-ABM tasks in an augmented reality environment. The games are progressed through rounds, during which users can interact with objects and earn points.
We implemented Uplifting Moods with the concept of a low-polygon, cartoon-style casual game and followed consistency in user interface design (see Figure 2) [39]. Combining visually appealing and consistent game art, happy and uplifting background music, and proper sound effects, the outcome of the design achieved an aesthetic game as a whole. Every condition and corresponding application are implemented using the Unity3D game engine (v. 2020.3.5f1).

2.2. Experimental Design

2.2.1. Participants

Twenty-five adult participants were recruited based on a power analysis that indicated a required sample size of twenty-two participants for F tests (repeated measures, within factors) with an α value of 0.05, a β value of 0.2, and an estimated effect size of 0.45. The participants were asked to fill in questionnaires and participate in experimental sessions and follow-up interviews. One participant was excluded due to the battery issue of their mobile phone. The final sample consisted of data from 24 adults (13 females, 11 males) aged 24 to 59 (M = 31.375, SD = 8.7268). For recruiting participants who may have state anxiety, a simplified version of the Highly Sensitive Person Scale (HSPS) [40] was used as a screening tool to recruit participants who scored above 65 (people who scored above 70 are considered having sensory processing sensitivity) as it has been proven that highly sensitive people may also experience higher levels of anxiety [41,42]. The scores of HSPS were used for screening purposes only.

2.2.2. Tasks and Experiment Protocol

Figure 3 shows our experiment protocol for this study. Prior to experimental sessions, participants received the information sheet, consent form, and simplified HSPS, questionnaires on baseline demographic data. At the beginning of the experiment, introductions to the study procedure and instructions for using Uplifting Moods were given to the participants. The goal of all game types is to touch targeted objects and avoid non-targeted ones to get the highest score. Participants were informed that clicking quicker would get higher points. Game stories of the game types (conditions for IVs) are summarized below:
  • Game Type A and B (Petting Lively Characters with Positive Emotion; AR interactive mode and regular mode): Players should interact with happy dragons and avoid angry dragons. By touching the happy dragons players receive rewarding points (faster interaction boosts the points; i.e., higher points). By contrast, touching angry dragons will lose points.
  • Game Type C and D (Collecting Objects without Emotion; AR interactive mode and regular mode): Players should only collect mushrooms among all the plants (which are the food for dragons). Collecting the right food will get points (faster interaction boosts the points; i.e., higher points). Collecting wrong foods will lose points.
The present study uses a within-group design, in which all the participants were required to attend four trials, including Game A: Petting Dragons in AR mode (ABM, AR), Game B: Petting Dragons in regular mode (ABM, non-AR), Game C: Collecting Food in AR mode (non-ABM, AR), and Game D: Collecting Food in regular mode (non-ABM, non-AR) in a random and counterbalanced order. When playing the games, the participants get visual feedback from their actions based on animations of objects and changes in UI (update of score and timer text). During the 3-min playing time in each of the games, positive (targeted) and negative (non-targeted) objects appeared 50% and 50% at random locations. The number of objects gradually increased from minimal 2 to maximal 8 in 47 rounds. The time for each round gets faster from 6 s to 4 s. Background music and sound effects were also exploited in the game. When users touch the targeted positive object, a positive sound plays. When they touch the non-targeted objects, a negative and buzzer sound plays.
Before and after playing each of the games, the participants were asked to fill in levels of arousal, valence, and dominance in a visualised 9-point scale Self-Assessment Manikin (SAM) [43]. The participants had more than 3-min breaks between each of the 4 sessions until they feel relaxed. After the four experimental sessions, semi-structured user interviews were conducted to dive into participants’ feelings about each of the games, preferable and unlikable features, and their experiences regarding playing games generally and mental wellness. For analysing the results thoroughly by understanding each participant further, a questionnaire with the Gamification User Types Hexad Scale [44] (which is used to categorize users based on their intrinsic motivations for engaging with gamification systems) and a question about their preference of four games was additionally sent to the participants one to two weeks after the study sessions. All participant data were fully anonymised and processed and analyzed in compliance with General Data Protection Regulation (GDPR) and ethical guidelines under our ethics approval (ID Number: UCLIC/1920/006).

3. Results

We analysed the collected data from the questionnaires, SAM, and the interviews to examine the efficacy of AR in mood changing in the four game types (along with IV conditions), relations between AR and ABM, the usability of Uplifting Moods, and the insights for designing an AR ABM game. Table 2 shows participant demographics, HSPS results, ranking of four games, frequency of playing games, and User Types based on Marczewski’s Player and User Types Hexad [45]. The statistical analysis was conducted with IBM SPSS Statistics (version 29.0.2.0).

3.1. Self-Assessment Manikin

Descriptive statistical analyses on SAM results of baseline (data before the first trial) and after four experimental sessions were conducted (Valence from Baseline: mean 5.87, SD 1.51; Valence from Game Type A: mean 6.5, SD 1.86; Valence from Game Type B: mean 6.41, SD 1.47; Valence from Game Type C: mean 6.5, SD 1.58; Valence from Game Type D: mean 6.54, SD 1.35; Arousal from Baseline: mean 3.95, SD 1.70; Arousal from Game A: mean 5.45, SD 1.69; Arousal from Game B: mean 4.75, SD 1.79; Arousal from Game C: mean 4.5, SD 1.71; Arousal from Game D: mean 4.54, SD 1.64; Dominance from Baseline: mean 5.54, SD 1.66; Dominance from Game A: mean 5.70, SD 2.05; Dominance from Game B: mean 6.58, SD 1.90; Dominance from Game C: mean 6.29, SD 1.80; Dominance from Game D: mean 6.87, SD 1.98). Boxplots for valence, arousal, and dominance rating distributions from after sessions across 24 participants for 4 games are shown in Figure 4.
A Shapiro–Wilk test showed that valence, arousal, and dominance ratings from all four games of Uplifting Moods are not normally distributed (p < 0.05). We therefore performed a non-parametric Friedman test on the values of valence, arousal, and dominance to analyse the differences of participants’ perception in the four different games. Significant effects were found on changes of arousal (χ2 = 13.5, p = 0.004, η2 = 0.37) and dominance (χ2 = 16.11, p = 0.001, η2 = 0.41) among the four games, whereas there was no significant difference in valence values (χ2 = 0.638, p > 0.05).
Post-hoc pairwise comparisons using Wilcoxon signed ranks tests were performed to compare the repeated measurements AR and ABM in the four games (See Table 3, including eta-squared used to calculate the effect size for each comparison). A Bonferroni correction was applied to adjust for multiple comparisons (k = 6), resulting in a significance level of 0.0083 (0.05/6). The results showed the AR ABM (Game A) made users feel activated (higher arousal rate) and less dominated. There were significant differences on arousal levels between the pairs: A-C (p = 0.001), A-D (p = 0.008) with an approaching significance in the pair A-B, confirming the AR feature leading to more aroused states. Further, we conducted pairwise comparisons on dominance and found a significant difference between the A-D pair (p = 0.008). Interestingly, participants reported feeling more under control without the AR feature; this finding is further supported by the qualitative study in the following section, where the higher difficulty level of Game Type A led some participants to feel less under control. Possible explanations for this trend are discussed in the corresponding section.
Further, we conducted valence-arousal dimensional analysis. Arousal (i.e., the intensity or activation level of the emotion) and valence (i.e., the pleasantness level of the emotion) are widely recognized as key dimensions of emotional experience [43,46], with various studies highlighting their effectiveness in assessing emotional responses across a range of contexts, including mood intervention. Russell’s Circumplex model of affect [46] is a widely used exemplary framework that has two dimensions (with the valence dimension in the horizontal axis and the arousal dimension vertical axis). This allows researchers to predict how individuals might respond to certain stimuli based on their emotional state. Based on the SAM, which helps measure arousal and valence levels [43], we dived into the changes in ratings from baseline and after sessions. Figure 5 shows scatter plots of ratings in the valence-arousal dimension. Central points of baseline and after session data were created with the means of the corresponding ratings. From the Euclidian distances calculated from the central points, we confirm that moods from the AR ABM intervention (Game Type A) particularly improved, with the longest distance 1.625 to the point of “cheerful” state on average, while the distances (mood changes) from other three interventions were 0.886, 0.827, and 0.827 respectively (all “pleased” state on average). Compared with the other interventions, the proposed AR ABM intervention lifted the users’ mood more by increasing their arousal moderately to the optimal arousal level. Note that when people are in the optimal arousal state, they not only feel more energised but also more focused on their task performance [47].

3.2. Game Preference

Figure 6 shows a stacked bar chart showing participants’ preferences in relation to the four games. It clearly shows that the proposed AR-enabled ABM (Game Type A) was the most preferable intervention, with about half of the participants (seven male, four female) rating that they liked it the most. Non-AR ABM (Game Type B) is the second-most popular one, with ratings from six participants (one male, five female). On the other hand, only three and four participants rated Game Type C and Game Type D (non-ABMs) as their favourite game among all the games, respectively. We can see that the male participants tended to like Game Type A, in which there are seven ratings from the males. The numbers of female participants who liked Game Type A and Game Type B were almost the same (four or five female participants). Further, Figure 7 shows the preference results with the Gamification User Types Hexad Scale [44], which assumes six different user types are motivated differently by game design elements (either intrinsic or extrinsic motivational factors) [45]. Given that the numbers of different user types among the participants were uneven, a comprehend analysis based on the user types could not be conducted. We discuss in the discussion section how personalisation could benefit different user types in the ABM intervention.

3.3. Qualitative Analysis Results

We conducted a thematic analysis [48] on the semi-structured interview data to identify key themes in support of the quantitative results and answering research questions. The recordings of the interviews were transcribed and thoroughly analysed. There were three focused topics. The first is about the software interfaces, including how the participants felt when they played each of the games and what features they liked and did not. The second and third parts are about participants’ perception when comparing the AR interactive modes with the regular modes and comparing ABM with non-ABM, respectively.

3.3.1. Positive Feelings About Uplifting Moods Gamification Interfaces

Simple and Relaxing Nature: Participants highlighted that the reasons they liked Uplifting Moods were its simple gamification concept with its clear goal, which made individuals to feel calm and the process to feel therapeutic and rewarding: “I really enjoyed playing them, I like the fact that when I touched them (the objects), they disappeared. I really enjoyed that they are disappearing, it’s like collecting them. I felt much calmer and more concentrated on.” (P4), “It’s good that the way of playing these games is simple, I don’t have to think too much.” (P5), “I like the fact that they keep moving around. Sometimes I feel that doing things repeatedly is therapeutic.” (P6), and “The game itself is quite simple, so I will look at the moving objects. After a while, I felt a bit more relaxed, although I don’t know the reason.” (P8).
Aesthetic Gaming Arts: The game art of Uplifting Moods is another key element that the participants liked, including the vivid and cartoony visual style, adorable and lively characters with interactive animation, relaxing and uplifting music, and effective sound effects. About game style, 13 participants highlighted that the visual style and artistic effects associated with ABM were favourable. For instance, “I think the style of the game is appealing and interesting.” (P2), “The visual style is cool! It’s like the polygon style.” (P14), “I really like the characters; both the character design and the animation are good! The angry dragon and the happy dragons are well-designed and cute.” (P3), “The animation is very clear, enjoyable and vibrant, reminded me that the video game I used to play.” (P17). Further, most of the participants found the music relaxing, peaceful, and comforting: “The music was comforting” (P1, P13, P14, P20), and “I think the music made me feel relaxed and I am playing a relaxing game.” (P21). Participants found the sound effects were satisfying: “I also like the fact that when you press on bad ones you will get the noises that you know that one is wrong.” (P15), “The colours and music are fun and therapeutic, which made me want to play more.” (P16)

3.3.2. “Petting Lively Characters with Positive Emotion”-Focused (AR-Enabled ABM)

Aligned with the quantitative findings, positive perceptions towards Game Type A (AR-ABM) were found in the interviews. As the most preferable game type, Type A was found to require more attention, effectively distracting the users from others (e.g., anxiety threats) and improving their moods overall. Nine participants highlighted its challenging and fun nature: “Petting Dragon AR mode is more difficult. Sometimes the dragons will be out of the screen or very close to the screen, which makes the game more challenging and fun. I found it quite funny and interesting.” (P1), “AR mode is more amusing and novel! It’s easier to immerse myself into the game. I felt AR impressed me more, especially Petting Dragons. The dragons were flying up and down, which is more difficult so when I caught them, the feeling was stronger.” (P3), “The level of Petting Dragons AR mode is higher because dragons are flying around and blocking each other. While I was playing, I felt more excited.” (P7), “Dragons in AR is more exciting and difficult, which makes the fun time last longer.” (P22).
Participants identified that it was effective in attracting their attention, having positive distraction effects that made individuals feel more relaxed. “(Petting Dragons AR mode) It requires higher concentration for me to wait until the timing which I can touch happy dragons.” (P7), “Like today I fretted about many things in my head. But when I was playing Petting Dragons (ABM), I felt it distracted me from the annoyance. After playing it, I could feel that my mood improved. Things that are not getting in control do not bother me that much now. I felt that the mood changes from upset back to positive.” (P16), “I think I was more focused on the game when playing AR mode because it requires me to actively move my phone around.” (P21).

3.3.3. AR Interactive Mode vs. Regular Mode

Participants felt that the AR interactive mode is exploring, engaging, and interactive, especially when it applies to the ABM game. This amplifies the quantitative findings that AR options are preferable and strongly induce higher arousal states. Particularly, it adds “fun” elements with higher interactivity: “AR mode comparing non-AR, I think non-AR is rather easy and AR mode is much fun. I felt AR feature changed my emotions positively.” (P10), “Playing Petting Dragons in AR mode is more like playing a fun game. Without AR feature it was not that fun.” (P13), “AR mode made me more focused and motivated on playing the game. I also find AR is a novel feature, it is very different from other mobile apps.” (P16), “When playing non-AR version games, there was not much excitement because there was some time to wait for dragons and mushrooms showing up.” (P21), “AR mode has higher interactivity, probably because I have to move my body, which brings benefits to relaxation. Without AR mode, it’s like only blankly staring at the screen and clicking.” (P19), “I felt happy when dragons showing in my surrounding. Like they are accompanying me, which makes me concentrate on playing the game.” (P16).
The findings above can be backed by AR’s more challenging and explorative nature, highlighted by participants: “When playing non-AR games, due to their easiness, I felt conscious of the time and thinking about when will the game finish? This didn’t happen when I was playing AR mode because it was much more fun and allowed me to explore more areas and look for the targets.” (P3), “I like that I was scanning my room and I can choose where I am going to play with the scene that is going to be set.” (P4), “I think playing the 2 games in AR mode have more exploring experiences which made me more focused on playing. AR mode provided a wider area to play, it’s very impressive and fascinating.” (P7), “The playing area of the AR mode is out of the phone screen, so it made me move my phone around to see the dragons. I found that it increases the challenge level of the game, which is quite good.” (P14).
On the contrary, only a few participants preferred the regular mode (no-AR) due to its easiness of playing and controlling. This also reflects on the results of dominance; the AR feature made some of the participants feel dominated after they played. “I found myself was focusing on the non-AR games more easily.” (P5), “I found AR mode is hard. I’d never played any AR games before. When I was playing, I had to move my phone and touch the food or dragons, it’s very hard. The other 2 games in regular mode then were better, like very easy and good.” (P9). Interestingly, P2 mentioned that “Playing Non-AR games allows me sitting, which made me feel more dominant”, while the participant preferred the AR interactive mode more. How to improve the designs will be discussed in the implication section below.

3.3.4. ABM vs. Non-ABM

Lastly, we further delved into comparisons between ABM and non-ABM game types. Participants who preferred the ABM (i.e., Petting Lively Characters with Positive Emotion) perceived that the happy dragons made them actually happier, most likely due to the lively emotions expressed by the dragon characters. “Compared to playing Collecting Food non-AR, playing Petting Dragons non-AR makes me happier! Not sure why that is.” (P5), “Comparing Collecting Food and Petting Dragons, I found that I want to play Petting Dragons more. Probably because of the design. Dragons have facial expressions and are more interactive, flowers and mushrooms are just two normal objects. When I was playing Collecting Food, I found it cute at the beginning, but after a while, I felt it was a bit too much. I guess it’s because there are no emotions in the plants.” (P16). “The food one (non-ABM) was too repetitive.” (P24).

4. Discussion

Designing an engaging ABM game could be challenging as ABM is repeated training that may not easily attract users. This study provides evidence that AR can increase the effectiveness of gamified ABM training in improving one’s mood. The quantitative analysis confirmed that there existed the immediate positive effect of ABM (both AR and non-AR) on mood in general [12,15,19], while it revealed that AR significantly amplified the positive shift in comparison with other options, including non-AR ABM and non-ABM gamification, during their short-term intervention. One relevant study suggested that gamified ABM would be effective in decreasing negative mood only when reduced vigilance was observed [12]. AR may contribute to reducing vigilance given its interactive and immersive nature, which adds an element of enjoyable exploration and may help users loosen their careful watch for negative threats during training. Indeed, participants found the proposed AR-enabled ABM to be more challenging and fun, with both elements often being associated in the literature [49].
Further, the provision of AR in training helped keep users motivated, and brings certain benefits of feature personalisation, meeting different individuals’ needs [44,45], which made it most preferable among the gamification types in this study. Nonetheless, personal preference still needs to be met in gamified mood intervention, as in general interactive technology and game designs [50]. While the AR-enabled gamified ABM led to the most positive outcome overall across different Hexad user types in gamification, offering it as an option would be of use in accommodating each individual user (e.g., during our interviews, two participants felt nervous in AR due to unfamiliarity) and enabling it to be more accessible.

4.1. Implications for Designing ABM Mental Wellbeing Interventions

To help researchers, software developers, and designers to implement AR-enabled ABM gamification for mental wellbeing management in an engaging way, we discuss design implications on three key elements below.
Aesthetic and Music: the design of games, including visual style and music, is an important way to tell a story that makes users feel connected and create affection towards the game, which may increase the meaningfulness of playing and users’ engagement [20,51]. In the interviews conducted after the participants played Uplifting Moods, most of the participants highlighted that they liked the game art and enjoyed the music and sound effects of the game, and they felt that the adorable gaming arts and amusing music made them happy and relaxed. Studies also show that the pleasure cycle of music involves brain regions that comprise the reward system [52,53]. The fact that music is effective in deriving pleasure [20] could be one of the reasons why no significant differences were found in the valence values after playing the four games in Uplifting Moods since all the background music was the same, being pleasurable for the participants.
Level Progression: Games with progression mechanisms could make the gaming experience more exciting [52]. Designing proper and achievable levels in ABM games would be beneficial in avoiding the repetitiveness of ABM and attracting users. As P4 commented on Uplifting Moods: “I like that the game speed-ups as I go through the games, it starts quite slowly, like at the beginning only a few mushrooms and then there were much more mushrooms, like in a harder level, I like that there was a progress, it’s enjoyable.” P10 also suggested that: “The game is rather simple and easy; maybe more levels could make a difference. Like getting harder or showing different characters etc.”
Socialisation: Social involvement with other players has been found to increase the meaningfulness of playing [51]. For some of the participants in this study, socialising with friends motivates them to play games. Functions like sharing their score with friends and having a leaderboard could potentially bring more fun to the game (P7 and P17 suggested this during the interviews). However, these competitive types of game elements should be carefully designed because it is important to avoid making users too nervous or excessively competitive, which induces anxious feelings when they want to achieve better performances to compete with their friends [54]. Making the game collaborative instead of competitive may be a good way to not only attract users who like socialising to play but also make the gaming process more enjoyable and rewarding, which may result in stronger intentions to play the game again [55].

4.2. Limitations and Future Work

Some important limitations in this work can guide potential future work. First, this study mainly focuses on the short-term effects of Uplifting Moods, in particular mood changes immediately following its intervention. While the immediate effects are promising, it is also very interesting to explore its sustained benefits over time. Also, the lack of a passive control condition (no intervention) may make it difficult to determine whether the observed outcomes are due to the intervention itself or other confounding factors such as social factors. Thus, future studies could involve a longitudinal study with a control group and follow-up sessions to check whether the mood improvements persist beyond the short-term post-intervention period and whether repeated use could result in lasting changes in emotional processing.
Second, the performance of the AR intervention may vary as it relies on device compatibility. Different mobile phone devices and capacity may lead to different playing experiences in AR mode, thus affecting the effectiveness of the intervention. Future research should consider standardizing the devices used or exploring how differences in devices might affect the intervention’s effectiveness. Also, user interface enhancements should be important in ensuring that the intervention is accessible to a broader audience, particularly for individuals with varying levels of comfort and familiarity with AR.
We recognise that the pre-selection of participants based on the HSPS may introduce a potential bias. By selecting participants who score highly on this scale, the sample may not fully represent the broader population, limiting the generalizability of the findings. We believe future research will benefit from including a more diverse sample beyond pre-selection criteria and larger sample sizes to validate and extend our findings. This would help develop a more comprehensive understanding of how the intervention may affect individuals with varying sensitivity levels.
Lastly, while we focused on self-report measures and calculated Hexad User Types of the participants to better understand each individual with in-depth interviews, they could be inherently subject to biases, such as social desirability and individual differences in emotional awareness and interpretation, possibly complicating the interpretation of the results. In future research, we recommend incorporating additional objective measures such as physiological parameters (e.g., heart rate variability) and behavioural observations (e.g., facial expression) to complement subjective data, which are indeed important in personalisation and effective intervention [56,57,58]. Future work will also benefit from advanced machine learning approaches [59,60] in tracking one’s physiological signatures and offering micro-intervention for better mental wellbeing on a daily basis.

5. Conclusions

In this paper, we have presented our novel AR-enabled gamified ABM approach, Uplifting Moods. The data collection and mixed method study analysis results showed that participants tended to feel more cheerful after playing the AR-ABM game type (condition A) with significantly increased arousal levels and fun, exploring, and challenging features. The same type was also rated as the most preferable game compared to the other three game types. Design implications for making AR-enabled ABM games effective and interactive were then developed, which focused on characteristics of aesthetics, music, level progression, and socialisation. Overall, this study has contributed to the understandings of how the AR feature and gamification provide benefits in ABM and contribute to the possibility of building greater effectiveness in novel treatments and improvements in people’s mental wellbeing in real-life contexts.

Author Contributions

Conceptualization, Y.C. and S.S.J.; Investigation, Y.J.Y., S.S.J. and Y.C.; methodology, Y.J.Y. and Y.C.; design artefacts, Y.J.Y.; data collection, Y.J.Y.; data analysis, Y.J.Y. and S.S.J.; manuscript preparation, Y.J.Y., S.S.J. and Y.C.; manuscript revision, S.S.J. and Y.C.; overall supervision, Y.C.; project administration, Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

S.S.J. was partly supported by the RRTAI academic research collaboration programme (RRTAI-0B23). The APC was exempted for this publication as an invited paper.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the University College London Interaction Centre (ID Number: UCLIC/1920/006/Staff/Cho) on 9 June 2020.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Extra data (other than those directly presented in this article) are unavailable due to privacy or ethical restrictions.

Acknowledgments

Authors thank our participants who participated in the study.

Conflicts of Interest

Author Sarah S. Jo was employed by the company RRTAI. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ABMAttention Bias Modification
ARAugmented Reality
HSPSHighly Sensitive Person Scale
SAMSelf Assessment Manikin

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Figure 1. Conceptual framework of Uplifting Moods. The arrow indicates its conceptual connection.
Figure 1. Conceptual framework of Uplifting Moods. The arrow indicates its conceptual connection.
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Figure 2. Uplifting Moods: a novel approach that combines Attention Bias Modification (ABM) gamification and Augmented Reality. (A) The mobile AR interface automatically recognizes a surface to present virtual characters for ABM, compared with (B) Non-AR ABM, (C) Non-ABM AR, (D) Non-AR, Non-ABM alternatives.
Figure 2. Uplifting Moods: a novel approach that combines Attention Bias Modification (ABM) gamification and Augmented Reality. (A) The mobile AR interface automatically recognizes a surface to present virtual characters for ABM, compared with (B) Non-AR ABM, (C) Non-ABM AR, (D) Non-AR, Non-ABM alternatives.
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Figure 3. Study Protocol. The arrow indicates the order of the study protocol stages.
Figure 3. Study Protocol. The arrow indicates the order of the study protocol stages.
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Figure 4. Boxplots for valence, arousal, and dominance rating distributions from after sessions across 24 participants for 4 games. The symbol * with line describes significant differences (p < 0.05) and * with dotted line describes approaching significance.
Figure 4. Boxplots for valence, arousal, and dominance rating distributions from after sessions across 24 participants for 4 games. The symbol * with line describes significant differences (p < 0.05) and * with dotted line describes approaching significance.
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Figure 5. Scatter plots of ratings from baseline and after sessions from the four sessions.
Figure 5. Scatter plots of ratings from baseline and after sessions from the four sessions.
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Figure 6. Stacked bar chart for participant preferences of the four games.
Figure 6. Stacked bar chart for participant preferences of the four games.
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Figure 7. Stacked bar chart for preferences of four games along with Hexad User Types.
Figure 7. Stacked bar chart for preferences of four games along with Hexad User Types.
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Table 1. 2 × 2 conditions with two independent variables (IV), ABM and AR (with each having two conditions), leading to four different game styles: AR-enabled ABM, Non-AR ABM, AR-enabled Non-ABM, and Non-AR Non-ABM.
Table 1. 2 × 2 conditions with two independent variables (IV), ABM and AR (with each having two conditions), leading to four different game styles: AR-enabled ABM, Non-AR ABM, AR-enabled Non-ABM, and Non-AR Non-ABM.
Independent Variables (IV)ARNon-AR
ABMGame Type A + Petting Lively Characters with Positive Emotion + AR Interactive modeGame Type B + Petting Lively Characters with Positive Emotion + Regular mode
Non-ABMGame Type C + Collecting Objects without Emotion + AR Interactive modeGame Type D + Collecting Objects without Emotion + Regular mode
Table 2. Participant demographics, HSPS results, ranking of four game types (the IV conditions), frequency of playing games, and Hexad User Types.
Table 2. Participant demographics, HSPS results, ranking of four game types (the IV conditions), frequency of playing games, and Hexad User Types.
AgeGenderHSPSRanking of the 4 Game TypesFrequency of Playing GamesUser Types
P133M86A > B > C = DEverydayFree Spirit
P225M92A > C > B > DAlmost everydaySocialiser
P326M91A > C > B > DAlmost everydayPhilanthropist, Socialiser, Player
P429F87B > C > D > ARarelyFree Spirit, Achiever, Disruptor
P528F103B > D > C > ARarely Player
P627F87C > A > B = DEveryday Player
P730M90A > C > D = BAlmost EverydayFree Spirit
P828F120B > A > D > CRarely Philanthropist
P926F118D > B > C > AEverydayPhilanthropist
P1058F98A > B > C > DRarely Philanthropist, Free Spirit
P1159M73D = B > C = AEverydayPhilanthropist
P1228M93B > D > C = AEverydayFree Spirit
P1332F83A = B > C > DOccasionallyFree Spirit
P1433M65A > B > C = DTwo or three times a weekAchiever
P1535F106B > D > C > AOccasionallyPhilanthropist
P1629F102A > C > B > DTwo or three times a weekPhilanthropist
P1729M98D > A > B > CRarelyPhilanthropist, Achiever
P1825F81C > A > D > BOccasionallyAchiever
P1927M92A > C > D > BOccasionallyPhilanthropist
P2029F93B > C > D > AEverydayPhilanthropist
P2127F103A > C > B > DAlmost EverydayPlayer
P2230M95A > C > B > DRarelyPlayer
P2330F81D > B > A > CEverydayFree Spirit
P2430M102C > A > B > DEverydayPhilanthropist, Free Spirit
Table 3. The results of Wilcoxon signed ranks tests comparing the two repeated measurements AR and ABM in the four games (p-values with η2 eta-squared used to calculate the effect size for each comparison).
Table 3. The results of Wilcoxon signed ranks tests comparing the two repeated measurements AR and ABM in the four games (p-values with η2 eta-squared used to calculate the effect size for each comparison).
ConditionsGame Type A vs. Type BGame Type A vs. Type C Game Type A vs. Type DGame Type B vs. Type CGame Type B vs. Type D Game Type C vs. Type D
Valencep = 0.726
(η2 = 0.351)		p = 1
(η2 = 0)		p = 0.899
(η2 = 0.127)		p = 0.658
(η2 = 0.443)		p = 0.499
(η2 = 0.676)		p = 0.948
(η2 = 0.066)
Arousalp = 0.045
(η2 = 0.25)		p = 0.001
(η2 = 0.822)		p = 0.008
(η2 = 0.508)		p = 0.538
(η2 = 0.027)		p = 0.441
(η2 = 0.049)		p = 0.928
(η2 = 0.001)
Dominancep = 0.019
(η2 = 0.322)		p = 0.138
(η2 = 0.129)		p = 0.008
(η2 = 0.395)		p = 0.365
(η2 = 0.048)		p = 0.163
(η2 = 0.139)		p = 0.044
(η2 = 0.253)
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Yeh, Y.J.; Jo, S.S.; Cho, Y. Uplifting Moods: Augmented Reality-Based Gamified Mood Intervention App with Attention Bias Modification. Software 2025, 4, 8. https://doi.org/10.3390/software4020008

AMA Style

Yeh YJ, Jo SS, Cho Y. Uplifting Moods: Augmented Reality-Based Gamified Mood Intervention App with Attention Bias Modification. Software. 2025; 4(2):8. https://doi.org/10.3390/software4020008

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Yeh, Yun Jung, Sarah S. Jo, and Youngjun Cho. 2025. "Uplifting Moods: Augmented Reality-Based Gamified Mood Intervention App with Attention Bias Modification" Software 4, no. 2: 8. https://doi.org/10.3390/software4020008

APA Style

Yeh, Y. J., Jo, S. S., & Cho, Y. (2025). Uplifting Moods: Augmented Reality-Based Gamified Mood Intervention App with Attention Bias Modification. Software, 4(2), 8. https://doi.org/10.3390/software4020008

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