Not All Spacings are Created Equal: The Effect of Text Spacings in On-the-go Reading Using Optical See-Through Head-Mounted Displays

Chen Zhou, NUS-HCI Lab, National University of Singapore, Singapore,
Katherine Fennedy, NUS-HCI Lab, National University of Singapore, Singapore,
Felicia Fang-Yi Tan, NUS-HCI Lab, National University of Singapore, Singapore,
Shengdong Zhao, NUS-HCI Lab, National University of Singapore, Singapore,
Yurui Shao, NUS-HCI Lab, National University of Singapore, Singapore,

The emergent Optical Head-Mounted Display (OHMD) platform has made mobile reading possible by superimposing digital text onto users’ view of the environment. However, mobile reading through OHMD needs to be effectively balanced with the user's environmental awareness. Hence, a series of studies were conducted to explore how text spacing strategies facilitate such balance. Through these studies, it was found that increasing spacing within the text can significantly enhance mobile reading on OHMDs in both simple and complex navigation scenarios and that such benefits mainly come from increasing the inter-line spacing, but not inter-word spacing. Compared with existing positioning strategies, increasing inter-line spacing improves mobile OHMD information reading in terms of reading speed (11.9% faster), walking speed (3.7% faster), and switching between reading and navigation (106.8% more accurate and 33% faster).

CCS Concepts:Human-centered computing → Empirical studies in ubiquitous and mobile computing;

Keywords: Text Spacing, Inter-line Spacing, Inter-word Spacing, Reading on-the-go, Mobile Reading, Heads-up Computing, OHMD, Smart Glasses

ACM Reference Format:
Chen Zhou, Katherine Fennedy, Felicia Fang-Yi Tan, Shengdong Zhao, and Yurui Shao. 2023. Not All Spacings are Created Equal: The Effect of Text Spacings in On-the-go Reading Using Optical See-Through Head-Mounted Displays. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), June 03–05, 2018, Hamburg, Germany. ACM, New York, NY, USA 19 Pages.


The future world we live in will likely be a blend of physical and virtual realities, creating a seamless and immersive experience [34, 94]. The exact details of how this will manifest are still being explored and debated, but increasing evidence has pointed to an emergent concept called the "metaverse" that blends these two realms. By allowing users to access virtual information while still being aware of their surroundings [53, 87], Optical See-Through Head-Mounted Display (OHMD), due to its heads-up and hands-free capabilities [35, 57], is a promising platform that can help users to more seamlessly explore and live in the metaverse. With OHMDs, users can now adopt a new heads-up interaction paradigm [89], allowing them to receive in-context, just-in-time digital assistance anytime and in any environment.

In order to receive and respond to digital information in real-time while performing daily activities, users need to engage in multitasking (e.g., on-the-go reading). Previous works [53, 64] have demonstrated this trade-off between reading and navigation performance; when OHMD reading performance increases, users’ perception of the environment tends to decrease. This imposes higher design requirements on how information should be presented in order to minimize interference with users’ primary tasks. Researchers have extensively investigated various aspects of text presentation, such as text colour [40, 41, 67], background colour [54, 87], font type [15, 31, 60], text position and layout [23, 68, 87], as well as the use of animation [54], to improve users’ reading experience on OHMDs in multitasking scenarios.

However, whether spaces within the text body can be further adjusted to improve the mobile OHMD reading experience is a research area that has been overlooked. We emphasise the word "further", as it is typically a typographer's job to determine the optimal spacing within the text. This includes spacing properties between words (inter-word spacing) and lines (inter-line spacing). Well-designed fonts have spacing arrangements that are often considered optimal, thus, rarely adjusted any further by users. Default spacing arrangements in typography are typically designed for conventional usage scenarios, while mobile reading on OHMDs is a unique use case with vastly different characteristics. This means that the default spacing arrangement may not be optimal for this new context. With this consideration in mind, we investigated strategies to further adjust inter-word and inter-line spacing within on-screen text blocks, with the goal of finding a presentation style to better support mobile OHMD reading.

Note that one reason spaces with text were previously under-explored is related to the length of the text. Previous studies have mainly explored the reading of small chunks of text (a few words to a sentence) on OHMDs [23, 54, 87] as they are easier to read during mobile multitasking scenarios. With only a small amount of text, there is limited need and room to further adjust spaces within the text. Yet if only small chunks of text were permitted to be displayed on OHMDs, it could be restrictive. While it is easy to display simple instructions or notifications, longer pieces of text (e.g., email, news article, recipe, etc.) need to be broken down into small chunks and displayed piece by piece, which has been shown by previous studies to significantly reduce user comprehension [32, 62, 85].

In our investigation, we first identified design factors (i.e., maximum range, alignment method) that affect text spacing for OHMD mobile reading in Study 1. Then in Study 2, we incorporated the inter-word and inter-line spacings within on-screen text blocks with the goal of finding an optimal presentation style to support reading and physical navigation multitasking. Finally, we compared the optimal text spacing interface (identified in Study 2) with two position-based strategies using navigation tasks of two levels of complexity in Study 3. We found that increasing the default spacing can improve mobile OHMD reading in simple and complex navigational situations. However, the effect differs between inter-word and inter-line spacing. While increasing inter-word spacing brings about negative effects, increasing inter-line spacing can significantly improve mobile OHMD reading and navigation performance (11.9% faster reading; 3.7% faster walking; 33% faster and 106.8% more accurate task switching between reading and navigation) as compared to position-based strategies. Based on the results, we discussed the reasons behind this phenomenon and design implications on how our findings can be used to improve the mobile OHMD reading experience.

In summary, our contribution is: we empirically explored the effects of text spacing on OHMD mobile reading, demonstrated that increased spacings are beneficial and identified the acceptable text spacing range, and investigated the optimal inter-word and inter-line spacing. We also compared our text spacing interface with two state-of-the-art position strategies (bottom-center and middle-right) using navigation tasks of two levels of complexity and demonstrated that the former benefits OHMD mobile reading performance.


2.1 Multitasking: OHMD Reading while on the go

Recent research revealed that Extended Reality (XR) would become mainstream in everyday life in the coming years [34, 94]. One type of wearable XR device is the emerging smart glasses platform or OHMD. Unlike traditional reading, OHMDs allow users to access digital information while still being aware of their surroundings [53, 87], making them especially useful for on-the-go situations [43, 58, 68, 87]. However, mobile OHMD reading requires users to multitask, dividing their visual attention between the OHMD display and their physical environment. The field of cognitive psychology has established that multitasking involves concurrent utilisation of a person's cognitive resources[71], which according to the Resource Competition Framework [70] can result in the slowing down, postponement, or termination of tasks when one is cognitively overloaded; a phenomenon referred to as the resource depletion penalty. Since real-life environments are often unpredictable and dynamic, physical navigation can impose significant mental and physical demands on users [64]. This may, in turn, compromise the user's ability to focus on the digital information presented on OHMD [53, 99]. In addition, the OHMD's screen is also transparent, which can make the text more difficult to read in dynamically changing environmental conditions such as lighting, background colour, and texture [40, 41]. Furthermore, existing OHMDs typically have a lower screen resolution than average mobile phones. Overall, these characteristics of OHMDs and their unique usage context mean that existing guidelines and practices in the user interface (UI) design may not be directly applicable. This concern motivates our study.

2.2 Text Presentation on OHMDs

To accommodate the specific characteristics of mobile OHMD reading, various properties of text presentation have been studied and redesigned. We discuss three categories that are most relevant to our study.

2.2.1 Text Style. Typeface, size, and format significantly affect perceptions of legibility, sharpness, and ease of reading in a computer-displayed text [15]. In the mobile OHMD reading context, previous studies [40, 56, 68] have shown that simple green text (#00FF00 in hex) against a black transparent background [54, 87]) is most visible and sans serif typefaces such as Arial are most readable [31, 40, 51]. Furthermore, a minimum size of 30pt at a distance of 2m was recommended for the Microsoft Hololens OHMD [8]. We incorporated these recommended text styles in our studies as control variables.

2.2.2 Text Layout. Various approaches have also been proposed to optimise text layouts for OHMD mobile reading. Orlosky et al. [68] suggested an intelligent system for automated positioning, such that text is placed in an on-screen position that complements the user's background environment. Similarly, Tanaka et al. [95] determined the optimal screen area for displaying information by using a mounted camera to evaluate the sight image behind the OHMD. While these methods aim to display information in ideal positions, they are responsive to the dynamically changing environment in which the user is present. This may lead to continuous changes in virtual object positions, which adversely affects the user's ability to multitask, especially in mobile situations. More closely linked to mobile OHMD reading scenarios are dual-task applications. Chua et al. [23] investigated the effect of notification positioning on monocular OHMDs and found that middle-right and top-right positions were most suitable for long-duration usage, while central positions were optimal for less urgent scenarios. Rzayev et al.’s [87] study extended these findings by demonstrating that the centre or bottom-centre positions decreased subjective workload and improved text understanding compared to the top-right position. Since mobile OHMD reading is a dual-task application that may be used for prolonged duration, we consider bottom-centre and middle-right positions to be comparable state-of-the-art strategies.

2.2.3 Text Quantity. The amount of text displayed on a single OHMD screen could affect user perception. According to Chen et al. [22], low-text quantities might include labels with a few words, while high-text quantities might consist of detailed description of objects. Previous studies on OHMD mobile reading have primarily focused on short texts (i.e., low-text quantities), such as presenting only several words [54, 87] or one sentence at a time [43]. While these strategies can help reduce the cognitive load associated with mobile reading, they limit the amount of content that can be shown.

Studies have shown that displaying small chunks of words on the screen can negatively impact users’ reading comprehension. Dillon et al. found that splitting sentences between pages often results in a frequent return to the previous page to reread the text [32]. This splitting will likely disrupt the comprehension process by placing an extra burden on the limited capacity of working memory. Additionally, 10-20% of the eye movements made when reading in this condition are regressions to earlier fixated words. Previous research [62, 85] also indicated that larger text sizes are more readable than smaller ones.

In the mobile learning context, Ram et al. [77] suggested that users can comprehend 6-8 chunks of the information displayed on-screen for controlling the information density. They further recommended that the information be persisted on the same screen to ease the temporal load on the working memory. Previous research has also highlighted the importance of data persistence for enhancing the understanding of information presented on OHMDs [66, 76], which suggests the potential value of displaying longer texts on these devices. However, Fukushima et al. [38] investigated presenting 10-line text using default spacing settings on an OHMD while walking on a treadmill and found that the text blocks displayed were challenging to read while walking and felt “overwhelming”. Therefore, we investigate how to adjust text spacing so that users can access longer text (e.g., emails or articles) intuitively and efficiently for OHMD mobile reading.

2.3 The Effect of Text Spacing on Reading

Reading is a complex cognitive process involving several mental processes, including visual perception, language comprehension, and memory [82]. Eye movements play a crucial role in reading, as they allow the reader to fixate on individual words and phrases, and to move smoothly from one part of the text to another [78, 79, 82]. The speed and accuracy of these eye movements can impact reading comprehension, and efficiency [55, 82]. The specific patterns of eye movements used during reading (e.g., z-pattern, f-pattern, and zig-zag pattern) can influence reading behaviour and comprehension [45, 46]. These patterns are determined by how people scan and focus on the words and phrases in a text, and can affect the speed and accuracy of the reading.

The z-pattern is a common reading pattern in which the reader's eyes move starting at the top left and moving horizontally to the top right, then diagonal down to the bottom left, and then another horizontal movement to the right. This pattern allows the reader to cover the entire page and fixate on each word [36, 45]. The f-pattern, on the other hand, is a more efficient reading pattern in which the first horizontal movement is similar to z-pattern, but the reader would scan a vertical line down the left side and fixate on keywords and phrases. If they found something interesting, they would read the line, forming the second horizontal line movement, and repeat the process [36, 65]. The zig-zag pattern extends the z-pattern by seeing it more as a series of z-movements instead of one big z-movement [45, 46]. The zig-zag pattern is particularly relevant in our case, as we asked participants to read the entire content and fixate on each word. They will continue to move to the right and then a little down and back to the left before starting another horizontal movement to the right again, which is how readers naturally read large blocks of text.

Additionally, typography involves various types of text spacing, including characters, words, and lines [7]. These are more suited to readings on conventional devices where users can devote their full attention. In this paper, we focus on two types of spacing that appear to have a greater impact on mobile OHMD reading: inter-word and inter-line spacing, as they guide eye movements and assist in reading speed and comprehension.

2.3.1 Inter-word spacing. Inter-word spacing refers to the space between words. It is an important factor in the way words are identified, as it helps the reader delineate between the beginning and end of words [75]. Inter-word spacings also guide eye movements and direct the eye towards target reading positions [81]. Previous studies affirm that inter-word spacings are essential, demonstrating a slower reading speed when removed [37, 74, 80, 81, 83].

2.3.2 Inter-line spacing. Similarly, inter-line spacing, which is defined as the space between vertically adjacent lines, assists in guiding eye movement [75, 81]. Previous studies found a slight increase in reading speed in peripheral vision for normally sighted individuals, but no improvement was found for reading using central vision in desktop reading scenarios [18, 21, 24].

Overall, the default inter-word and inter-line spacing are more optimal for conventional reading scenarios. Yet mobile reading on OHMD is a unique use case with vastly different characteristics. The default spacing arrangement may not be optimal for this new context. It remains inconclusive whether the same insights translate to real-world OHMD reading and physical navigation scenarios. This motivated us to delineate factors influencing mobile OHMD reading performance in a series of full lab studies.


We proceeded with our investigation in three steps (see Fig1(b)).

  1. Study 1 - Identifying design factors that affect text spacing on OHMD mobile reading. We conducted preliminary studies to investigate: 1) What are the benefits, if any, of increasing spacings beyond the default specified by font style? 2) What is the acceptable range for text spacing increments? Since we noticed that additional spacing in the horizontal direction could utilise two approaches (aligned vs. unaligned), we also conducted a pilot study to find 3) Which approach is more suitable for introducing inter-word spacing?
  2. Study 2 - Investigating the effects of text spacing on OHMD mobile reading. With the findings from our previous studies, we proceeded to more systematically investigate how inter-word or inter-line spacings affect mobile OHMD reading. Study 2 identified the optimal text spacing strategy, which we further validated in study 3 with existing approaches.
  3. Study 3 - Comparing text display method (Spacing vs. Positions) on OHMD mobile reading. In study 3, we compared the spacing strategy for text displayed in the centre of OHMDs, a display position that is not considered optimal for mobile OHMD reading with different complexities in walking paths. Existing literature suggests bottom-centre and middle-right as ideal positions to achieve a better balance in mobile multitasking scenarios. Thus, we empirically evaluated our proposed text spacing approach (identified in Study 2), with the two recommended approaches to further validate our findings.
The apparatus used and the study structure
Figure 1: (a) The apparatus included the NReal OHMD and iPad Mini tablet, which participants held as they performed the experiment. (b) A structure connecting the summary of our three studies.


All our studies share a common setting and we provide the summary below.

4.1 Tasks & Materials

Overall, the task is to simulate mobile reading on OHMDs while navigating an indoor environment (via walking). This larger task involves two subtasks: reading text from an OHMD's display and navigating towards a destination, based on pasted signs.

The reading subtask: Asking participants to read silently makes it difficult for experimenters to monitor the reading progress. Thus, we followed Ku's [54] approach in instructing participants to read text aloud, as fast and as accurately as possible while comprehending the content. The reading material presented on OHMD was adopted from the AceReader application [1]. To avoid potential confounding effects, we chose ten English articles under the “fun fact” category since these articles were less familiar to general audiences. AceReader was designed to offer articles of the same difficulty level, each with 8 well-calibrated multiple-choice questions (with 4 answer options each). To ensure the reliability and validity of the selected articles, we conducted a pre-test where we asked seven lab members to complete 160 multiple-choice questions (MCQs) without providing them with the original articles. The articles with the lowest scores were selected for the study. In addition, we conducted a post-test where we asked participants if they were familiar with the reading articles using a 7-point Likert scale to control for any potential effects of prior familiarity on their reading comprehension scores.

Each article has an average of 360 words (SD=11.1), with 14.6 words per sentence (SD=3.5), and a Flesch Score in the 65-70 range, which suggests it can be easily understood by 8th graders [4]. The text was displayed on an iPad and mirrored the OHMD screen. Display settings were chosen according to prior research. The text was green (#00FF00 in hex) against a black background that appears transparent on OHMD [40, 54, 56, 68, 87], with an Arial font of size 30pt [8, 31, 40, 51].

To navigate to the next page of text, participants simply needed to tap on the iPad screen without needing to look at the iPad.

Navigation subtask: In this study, participants were asked to walk along a rectangular path with two levels of complexity: simple and complex. Study 1 and 2 used simple walking path only, while Study 3 used both simple and complex walking paths. Participants circulated the path 2 times per condition (see Fig:2(a) and Fig:2(c)). A sign with 4 locations listed was pasted on each wall along their path. Depending on their current round of circulation (e.g. the second round), they had to read aloud the location listing with the corresponding number (e.g. they had to read out “2. Recording Room at Level 9” from the sign). As recommended [51], the signs showed 160 pseudo location names (21-31 characters, M=24.6, SD=2.09) with white Arial font against a black background.

During the study, we asked participants to read from an OHMD while navigating the assigned path. For the simple path, participants were asked to read and follow navigation signs as they passed by them. For the complex path, there were navigation signs placed to the left and right of the participant, and they were required to follow the direction of the signs to determine which one to read. They were allowed to stop and resume walking as and when necessary. Each trial began when the user read the first word of text on OHMD and ended only when the participant completed all rounds of walking, regardless of whether they finished reading the text on OHMD.

4.2 Devices & Software

One of our key considerations was to select the right OHMD model for our experiments, as we hoped to minimize technical limitations which may affect the OHMD reading experience. After evaluating 4 different OHMDs (Vuzix Blade, Epson BT300, Microsoft Hololens 2, and NReal Light), we picked the Nreal Light glasses [5], as it provided the best viewing experience in mobile walking scenarios. Our decision was based on several factors, including the weight, field of view, display resolution, and user comfort of each device. NReal is lightweight (106 grams) and has higher visual clarity, with a 52-degree diagonal field of view and stereoscopic display resolution of 1920x1080 pixels. In air casting mode, its screen is 115 inches diagonal at 3 metres, such that text can be clearly displayed, and comfortable to read for mobile uses.

Participants wore the Nreal Light glasses which mirrored the screen of a connected iPad Mini tablet [2]. React, Typescript, and Ionic were used to develop the experiment application hosted on the iPad Mini (Apparatus: see Fig1(a)). We compared the use of the iPad mini with alternative devices, such as smartphones and the Nreal smart glasses’ own controller. We chose the iPad mini because its screen mirroring was most similar to the Nreal glasses, and offered users better readability and simplicity. The iPad mini's small and light form factor was well-received by users and did not cause any complaints about holding it while walking. However, in situations where the users’ hands are occupied, it may be better to replace the iPad mini with a more portable device and use a wearable controller such as a ring mouse to interact with the OHMDs.

The mobile tasks used
Figure 2: The mobile tasks used. For both (a) simple and (c) complex walking tasks, the floors were taped to outline a rectangular path with a perimeter of 30 meters (width of 8m) that participants followed. Signs were pasted 2.5m away from the path, in the participant's line of sight. The sign changed accordingly in each study. For the simple path (a), participants were asked to read and follow navigation signs as they passed by them (b). For the complex walking task (c), there were navigation signs placed to the left and right of the participant, and they were required to follow the direction of the signs to determine which one to read (d). Note that Studies 1 and 2 used simple walking path only, while Study 3 used both simple and complex walking paths.

4.3 Common experimental procedure

All our studies shared similar experimental procedures. Thus, we lay down common steps below and if necessary, elaborate on additional details specific to each study in corresponding sections.

The common procedure starts with participants signing a consent form and completing a questionnaire about their demographic information. Next, we asked them to walk naturally for two rounds along the taped path to determine their normal walking speed. We then provided an example task to familiarise participants with the experiment as part of the training session. Afterwards, they began the actual experimental task. After completing a condition, we tested participants’ comprehension using the MCQs of their text article. Before proceeding to the next condition, we asked participants to complete a questionnaire relating to perceived task load, and also provided an optional 2-min break. After completing all formal conditions, we collected their overall preference ranking across all conditions. We asked participants to elaborate on their choices through our semi-structured interview, before concluding the experiment.

4.4 Measures

Measures can be categorised into four groups: 1) primary OHMD reading task performance, 2) secondary navigation (reading while walking) task performance, 3) overall task-switching performance, and 4) subjective measures in terms of task workload and overall preference ranking.

4.4.1 Primary reading task performance. This was measured using the reading goodput and comprehension. Similar to Ku et al. [54], we calculated reading goodput by dividing the total number of words correctly read out by the total time spent in each text spacing condition. Its units are words-per-minute (WPM). For comprehension, we measured the percentage of questions answered correctly [72, 86].

4.4.2 Secondary navigation tasks. This was measured using the Percentage of Preferred Walking Speed (PPWS) and reading task accuracy [43, 77]. For PPWS, we measured the walking speed of each text spacing condition and calculated the percentage based on the normal walking speed (measured at the beginning of the study procedure). For accuracy, we measured the percentage of locations correctly read out.

4.4.3 The task-switching performance. This was evaluated via:

  1. Switch-back Duration (SBD). This measures the average switching time between primary (OHMD reading) and secondary (navigation) tasks [70]. Specifically, we started measuring the duration at the completion of a secondary task and ended at the beginning of a primary task.
  2. Switch-back Error Rate (SBER). We measured the percentage of words repeated, skipped, or incorrectly read aloud while the participant resumed the primary task from the secondary task [13, 91].

4.4.4 Subjective measures.

  1. NASA-TLX. This procedure computes participants’ perceived task load [44].
  2. Overall preference ranking across all conditions.


We began our design exploration by conducting two preliminary studies. Study 1 answers the following research questions:

RQ1: Does increased text spacing better support OHMD reading while walking than default spacing?

RQ2: If increased text spacing is beneficial, what is the acceptable spacing range?

We assumed in this preliminary stage that spacings include inter-word and inter-line dimensions and that both equally influence OHMD reading while walking. We tested 4 spacing arrangements in 50-pixel increments applied equally in both directions: 0 (i.e. default or no additional spacing), 50, 100 and 150 pixels. A pilot study was conducted to test different spacing increments, and it was found that increasing the spacing by 50 pixels improved participants’ ability to distinguish between the different spacing conditions. We excluded 200 pixels as it severely limits the number of words that can be displayed on the OHMD screen (about 8 words) and adversely affects the reading experience [20].

5.1 Participants

We recruited 12 participants (6 females) between 19-28 years old (M=21.8, SD=2.42) from the university community. All participants had normal or corrected-to-normal vision with no colour deficiency and were fluent in English at the university level. None of them had prior experience using OHMDs. They were compensated at the standard rate of US$7.30 per hour.

5.2 Design

A repeated measures within-subject design was used. The independent variable was Text Spacing (see Fig. 3: W0_L0, W50_L50, W100_L100, W150_L150) and counterbalanced using a balanced Latin square to avoid potential ordering effects.

Study 1 Conditions
Figure 3: The four conditions compared in Study 1: inter-word (W) and inter-line (L) spacing vary by 0 pixels in (W0_L0), 50 pixels in (W50_L50), 100 pixels in (W100_L100), and 150 pixels in (W150_L150).

5.3 Results

Study 1 Results
Figure 4: Study 1 Results: Means and standard deviations (SD) of (a) Reading goodput, (b) Percentage of Preferred Walking Speed, (c) NASA-TLX workload, (d) Switch-back Duration, (e) Switch-back Error Rate for text spacings arrangements of 0 pixels in (W0_L0), 50 pixels (W50_L50), 100 pixels (W100_L100), and 150 pixels (W150_L150).

5.3.1 Primary Task Performance. This was evaluated by Reading Goodput (number of correct words read per minute) and Reading Comprehension Scores. In summary, uniformly increasing inter-word and inter-line spacing improves the mobile OHMD reading performance, but its benefits diminish as the spacing increases to 150 pixels, suggesting that while increasing text space can potentially be beneficial, there is an optimal range. Text spacings that go below or above this range may adversely affect performance. A detailed analysis is provided below.

Comparing the Reading Goodput (wpm), we found a main effect of Text Spacing (F3, 33 = 6.86, p < .05, $\eta ^{2}_{G}=0.06$) on Reading Goodput. Pairwise comparisons revealed that 50 pixels (M = 119.12 wpm) had a higher Reading Goodput than both 0 pixels (M = 103.46 wpm, p < .05) and 150 pixels (M = 104.26.26 wpm, p < .05) (see Fig. 4). 100 pixels (M = 116.56 wpm) also had a higher Reading Goodput than 0 pixels (M = 103.46 wpm, p < .05). However, no significant difference between 50 pixels (M = 119.12 wpm) and 100 pixels (M = 116.56 wpm) was found. This shows that text spacings are more suitable for mobile OHMD reading than the default spacing (0 pixels). However, text spacings should not exceed 100 pixels because an increase from 50 to 100 pixels resulted in only a 2.1% decline in reading performance, but increasing it from 50 to 150 pixels led to a 14.3% performance drop. In addition, there was no significant effect of Text Spacing on Reading Comprehension Scores, suggesting that although text spacings can affect Reading Goodput, it does not significantly affect comprehension.

5.3.2 Secondary Task Performance. This was measured using reading Task Accuracy and Percentage of Preferred Walking Speed (PPWS). In summary, we found a positive correlation between the amount of text spacing and navigation performance. In other words, the larger the text spacings, the faster and easier the navigation. Details are provided below.

There was no main effect of Text Spacing (p > .05) on Task Accuracy, indicating that participants were similarly accurate with spaced texts as well as without. Also, we found a main effect of Text Spacing (F3, 33 = 29.59, p < .001, $\eta ^{2}_{G}=0.12$) on PPWS (see Fig. 4). Pairwise comparisons revealed that text spaced with 150 pixels ($M=48.5\%$), 100 pixels ($M=46.9\%$), and 50 pixels ($M=45\%$) all resulted in a higher PPWS than 0 pixels ($M=40.7\%$) (all p < .001). In addition, 150 pixels ($M=48.5\%$) also incurred a higher PPWS than 50 pixels ($M=45\%$) (p < .05). The relationship between text spacing and walking performance is positively correlated (i.e. improvements in walking performance taper as text spacings increase), though not linearly proportional (i.e. increasing from 0 to 50 pixels led to a 10.6% improvement, while increasing from 100 to 150 pixels only yielded a 3.4% improvement). These findings are in line with the Resource competition framework (RCF) [70]; greater spaces between words provide visual gaps for readers to look at their physical environment. Thus participants were better able to navigate the environment, which explains the faster walking pace.

5.3.3 Overall Task-Switching Performance. This was evaluated using Switch-Back Duration (SBD) and Switch-Back Error Rate (SBER: Percentage of words repeated, skipped, or incorrectly read). In summary, we found that increasing text space improves task switching, but its improvement rate roughly followed a power curve (see Fig. 4) instead of being linear. In other words, the improvement is much greater with initial space increments but slowly approaches maximum benefit as the increment increases. Note that with a limited range, it is difficult to judge whether further increments of text spacing will approach a theoretical limit or reverse the trend.

The results of SBD and SBER exhibit similar trends. We found a significant main effect of Text Spacing on both SBD (F3, 33 = 14.43, p < .001, $\eta ^{2}_{G}=0.09$) and SBER (F3, 33 = 10.34, p < .05, $\eta ^{2}_{G}=0.30$). Pairwise comparisons revealed that text spaced with 50 pixels (MSBD = 1.25s, $M_{SBER}=4.3\%$), 100 pixels (MSBD = 1.03s, $M_{SBER}=2\%$) and 150 pixels (MSBD = 0.97s, $M_{SBER}=2.6\%$) incurred less SBD and SBER than 0 pixels (MSBD = 1.57s, $M_{SBER}=9.3\%$) (all p < .05). These results suggest that adding text spacings improves OHMD multitasking. Text with 100 or 150 pixels could save participants around 38.2% of task switch-back time and improve task switch-back accuracy by 76.3-78.5% as compared with default spacing (0 pixels).

5.3.4 Subjective measures: The NASA TLX results formed a pattern similar to task-switching performance. Its improvement rate roughly followed a power curve (Fig. 4) with much greater gains during initial space increment and slowly approaches maximum benefit as the increment further increases. A detailed analysis is provided below.

We observed a main effect of Text Spacing (F3, 33 = 9.99, p < .001, $\eta ^{2}_{G}=0.07$) on perceived Task Workload. Pairwise comparisons revealed that 50 pixels (M = 43.45), 100 pixels (M = 40.92) and 150 pixels (M = 39.28) had significantly lower task workloads than 0 pixels (M = 53.14) (all p < .05). This suggests that greater text spacings can reduce task workload (i.e. 50, 100, and 150 pixels outperformed 0 pixels). These findings also show similar trends to PPWS, SBD and SBER results.

In addition, participants were asked to provide feedback on two aspects, i.e. the most preferred and the maximum acceptable condition (upper bound of text spacings). All participants preferred having text spacings over default spacing. Of all spacing configurations, seven participants preferred 100 pixels as their first choice, while the remaining five preferred 50 pixels. Eight participants felt 150 pixels were their upper bound, while four chose 100 pixels.

5.4 Discussion

We draw the following conclusions based on our findings.

5.4.1 Increased text spacings outperform the default spacing arrangement, and improve OHMD multitasking performance. We found that greater text spacings (100 and 150 pixels) considerably outperformed default spacing in terms of reducing participants’ task workload, resulting in a significantly higher PPWS, and lower SBD and SBER measures of multitasking. This suggests that text spacing could positively influence the way in which participants shifted their attention between the digital display and the physical environment. Participants’ feedback showed that with increased text spacings, they could more easily view digital information while simultaneously locating their physical environment through the gaps between words. Besides, these spaces seemed to help participants detect task-switching boundaries, which caused less disruption and reduced the overall multitasking workload.

As a result, all participants preferred having some text spacing over default spacing. Participants found that default spacing made it “difficult to see the surrounding environment through the text” (P8, P9). Also, spacings between words “facilitate[d] smooth reading and switching between the primary and secondary tasks" (P1, P12).

5.4.2 There is an acceptable range for text spacings. Although different measures provided different conclusions, considered together, we conclude that the benefit of spacing increment lies within a certain range. Beyond its range, the overall benefits either plateaued or diminished. For example, as the spacing increased beyond 100 pixels, the reading performance decreased. While deciding whether to pick 100 or 150 as the suitable range, we looked at the data as well as consulted our participants. Based on the subjective feedback, eight participants found 150 pixels to be “too spacious” (P2, P9), and as it supported much fewer words per screen, content had to be spread “across multiple pages/screens” (P8). The downside of this is that participants had to click and scroll more frequently, which “[added to] more mental and physical demand” (P1). Taking into account objective results and subjective feedback, we chose 100 pixels as the upper limit for our follow-up studies.

Pilot Study Conditions
Figure 5: Three types of text display format were evaluated in the Pilot study: (i) W0; (ii) W100 Unaligned; (iii) W100 Aligned with 5 columns per screen.

5.5 Pilot Study

The logical next step is to investigate potential differences in inter-word and inter-line spacings for OHMD mobile reading. However, while inter-line spacing is straightforwardly defined, inter-word spacing can either be unaligned (the space between the last letter of a word and the first letter of the next word) or aligned (the space between the first letters of consecutive words is the same as the previous word that adopted from previous study [92], see Fig. 5). We further conducted a pilot study with 9 participants between the ages of 20-27 (Mean=23, SD=2.55) to understand if words should or should not be aligned horizontally for effective OHMD reading and walking. Results show that the unaligned arrangement (W100) was more suitable for inter-word spacings, as it significantly outperformed the aligned arrangement in terms of task-switching performance (61.64% improvement) and task workload (15.28% reduction). Further examination of the literature revealed that when all columns and rows are visually aligned, variable word lengths cause word boundaries to become less obvious to readers, thereby adversely affecting their reading performance [75, 80]. Additionally, our results revealed that text without inter-word spacing (W0) outperformed text with 100-pixel (W100) inter-word spacing in reading performance. This contradicts Study 1 results, which showed that equal inter-word and inter-line spacings could improve reading performance. An interaction effect between inter-word and inter-line spacings could be a plausible reason for this. This also suggests that inter-line spacing (applied to Study 1 conditions but absent in Pilot) could have a greater positive effect on reading performance than inter-word spacing. We investigated these possibilities in our Study 2.


Our previous studies’ results revealed two preliminary insights. Firstly, we found the desirable range for inter-word and inter-line spacings to be 0 to 100 pixels. Secondly, we found that the unaligned text was more suitable for inter-word spacings, yielding a lower task workload and improved multitasking performance compared to its aligned text counterpart. In addition, results and participant feedback suggest that the effects of inter-word and inter-line spacings are possibly not equal. To investigate potential interactions, we conducted a follow-up study with the following research questions.

RQ1: How do inter-line spacings affect OHMD reading and walking performance? Results from previous studies suggest that inter-line spacings have a positive effect on reading and walking performance. Thus, we hypothesise that:

H1.1: Inter-line spacing improves users’ reading performance.

H1.2: Inter-line spacing increases users’ walking speed.

RQ2: How do inter-word spacings affect OHMD reading and walking performance?

Study 1’s results indicate that inter-word spacings should not exceed 100 pixels, after which reading performance declines. This may be reflective of natural reading techniques, for which words are segmented into meaningful phrases as we read. Excessive increases in inter-word spacing necessitate more eye movements in this process. For walking speed, however, our pilot study (see section 5.5) suggests that inter-word spacings do not affect walking speed. Hence, we hypothesise that:

H2.1: There is an optimal value for inter-word spacings, outside which reading performance tends to decrease.

H2.2: Inter-word spacing does not affect users’ walking speed.

RQ3: Is there an interaction effect between inter-word and inter-line spacing for mobile OHMD reading scenarios?

Study 1 and pilot study (see section 5.5) suggest possible interaction effects between inter-word and inter-line spacing. Hence, we hypothesise that

H3.1: There is an interaction effect between inter-word and inter-line spacings.

H3.2: Inter-line spacing improves reading and walking performance more than inter-word spacing.

6.1 Participants

We recruited 18 participants (8 males) between the ages of 19 and 25 (Mean=21.5, S= 2.28) from the university community. None of them participated in previous studies and had prior experience using OHMDs. All other participant details follow that of previous studies.

6.2 Design

We used a within-subject design with 3 inter-word spacing levels (W0, W50, W100) x 3 inter-line spacing levels (L0, L50, L100), which resulted in 9 conditions per participant as shown in Fig. 6. The order of inter-word and inter-line spacing variables were counterbalanced using a balanced Latin square to avoid potential ordering effects. The same measures from previous studies were used.

Study 2 Conditions
Figure 6: 3x3 combinations of text spacing conditions were used in Study 2, including 3 inter-word spacings (W0, W50, W100) by 3 inter-line spacings (L0, L50, L100).

6.3 Results

The Switch-Back Error Rate (SBER) did not meet the normality assumption of ANOVA (Shapiro-Wilk tests, p > .05); thus, we applied the aligned rank transformation [98].

Study 2 Results
Figure 7: Study 2 Results: Means and standard deviations (SD) of significant measures: (a) Reading goodput for inter-word spacings, (b) Reading goodput for inter-line spacings, (c) Percentage of Preferred Walking Speed for inter-line spacings, (d) Switch-back Duration for inter-line spacings, (e) Switch-back Error Rate for inter-line spacings, (f) NASA-TLX workload for inter-line spacings.

There was no significant interaction effect of inter-word spacing x inter-line spacing (p > .05) on all measurements.

6.3.1 Primary reading performance: For inter-word spacing, we only found a main effect on Reading Goodput (F2, 34 = 17.27, p < .001, $\eta ^{2}_{G}=0.06$) (see Fig 7). Pairwise comparisons revealed that W0 (M = 113.08 wpm) resulted in the highest Goodput (all p < .001), followed by W50 (M = 106.9 wpm) and finally W100 (M = 99.26 wpm). Also, W50 was significantly higher than W100 (p < .05). This suggests that inter-word spacing reduces reading speed, which is in line with previous studies findings. For inter-line spacing, there was a main effect on Reading Goodput (F2, 34 = 49.97, p < .001, $\eta ^{2}_{G}=0.15$). Pairwise comparisons revealed that L0 (M = 93.09 wpm) resulted in the lowest Reading Goodput (all p < .001), but L50 (M = 111.1 wpm) and L100 (M = 115.05 wpm) were comparable (p > .05). These results suggest that while additional inter-word spacing reduces reading goodput, additional inter-line spacing increases it, but only for the first 50 pixels.

6.3.2 Secondary Task Performance: There was no main effect of inter-word spacing on Task Accuracy and PPWS, while we found a significant main effect of inter-line spacing on PPWS (F2, 34 = 71.16, p < .001, $\eta ^{2}_{G}=0.05$) but no effect on Task Accuracy (see Fig 7). Pairwise comparisons show that L100 ($M=43.33\%$) and L50 ($M=42.43\%$) had higher PPWS than L0 ($M=37.87\%$) (all p < .001, see Fig 7). This suggests that greater inter-line spacing could generate higher walking speeds.

6.3.3 Overall Task Switching Performance: There was no main effect of inter-word spacing on SBD and SBER. We found significant main effects of inter-line spacing on SBD (F2, 34 = 60.94, p < .001, $\eta ^{2}_{G}=0.27$) and SBER (F2, 34 = 8.54, p < .001, $\eta ^{2}_{G}=0.12$). Pairwise comparisons showed similar trends between both measures, which revealed that L100 (MSBD = 0.81s, $M_{SBER}=1\%$) and L50 (MSBD = 1s, $M_{SBER}=2.5\%$) had lower SBD and SBER measures of multitasking than L0 (MSBD = 1.55s, $M_{SBER}=5.13\%$) (all p < .001) (see Fig 7). In addition, L100 (MSBD = 0.81s, $M_{SBER}=1\%$) had a lower SBER than L50 (MSBD = 1s, $M_{SBER}=2.5\%$) (p < .001), which suggests that greater inter-line spacing can generate task switching speeds and accuracy in multitasking scenarios.

6.3.4 Subjective measures: The NASA TLX assessment revealed one main effect of inter-line spacing (F2, 34 = 26.39, p < .001, $\eta ^{2}_{G}=0.10$) (see Fig 7). Pairwise comparisons revealed that L100 (M = 34.83) and L50 (M = 35.75) both had a lower task workload than L0 (M = 45.9) (p < .001, each). This suggests that increasing inter-line spacing may help participants lower task workloads in multitasking scenarios. Participants’ preferences also showed a similar trend. Eleven participants preferred having only inter-line spacing (without inter-word spacing) between words: nine preferred W0_L100 while two preferred W0_L50. Yet, only six participants preferred combining both spacings: Five preferred W50_L100 (i.e. inter-line spacing larger than inter-word spacing) and one preferred W100_L100. Only one participant preferred W100_L0 (inter-word spacing without inter-line spacing) because excessive inter-line spacing made it difficult for her to move from one line to the next as she read.

6.4 Discussion

6.4.1 Inter-line spacings increase OHMD reading and walking speed, as well as overall multitasking performance. Our results show that text with inter-line spacings (of 50 and 100 pixels) considerably outperformed those without additional inter-line spacings (L0) in terms of task workload, reading speed, walking speed, as well as multitasking speed and accuracy. These benefits were also evident from participant comments. With increased inter-line spacing, they could “easily track where [they] stopped when switching back to the primary reading task” (P9 and P4). With small or no inter-line spacing, they “felt confused about where to locate the next line that should read” (P11 and P10). Overall, we find support for hypotheses H1.1 (inter-line spacing improves reading performance) and H1.2 (inter-line spacing increases walking speed).

6.4.2 Inter-word spacings reduce reading speed while walking. While almost all objective measures show no effect for inter-word spacing, participants produced the highest goodput with W0 conditions. In fact, adding 50 pixels and 100 pixels of inter-word space reduces goodput by 5.5% and 12.22% respectively. This undesirable effect is consistent with our qualitative results, where almost all participants (17/18) disliked W100_L0. As hypothesised, participants felt that the additional 50 pixels result in the eyes having “to move rapidly and [is] thus very distracting” [P9]. Distracted participants could, as a result, accidentally read from the wrong line. Therefore, we find no support for H2.1, which hypothesises that there is an optimal inter-word spacing. We consider the possibility that spacings between W0 and W50 (e.g. W10, W20, W30, W40) may influence outcomes and suggest this as a direction for future exploration. In addition, our results support H2.2, which hypothesises that inter-word spacing does not affect walking speed.

6.4.3 Inter-line spacing is more beneficial than inter-word spacing in OHMD multitasking scenarios. Inter-word spacing is more important than inter-line spacing in stationary reading contexts [18, 42], and our previous studies also hint towards the fact that this translates to the mobile OHMD reading situation. Despite this, our results do not support H3.1, revealing no interaction effect between inter-word and inter-line spacings on all measurements. In fact, our results reveal that inter-line spacings have significant positive effects on reading goodput, PPWS, task switching performance and task workload. On the other hand, inter-word spacings negatively affect reading goodput and have no effect on all other measurements. Hence, we find support for H3.2, that inter-line spacings are more advantageous than inter-word spacings.

From our studies so far, we have gained two main insights. Firstly, the mechanism by which inter-word spacings affect reading speed differs from that of inter-line spacings; while inter-word spacing influences the reading of consecutive words, inter-line spacing affects the reading of successive lines. Since the frequency of word-to-word reading is higher than that of line-to-line reading, “undesirable inter-word spacing could [more] easily have a negative effect on reading speed” (P11). Participants also found it challenging to locate words when switching between secondary and primary tasks (P7 and P15). Despite the negative effects of inter-word spacing on reading goodput, Study 1 showed that equal inter-word and inter-line spacings improved reading performance. This suggests that inter-line spacings can function to compensate for the negative impacts of increased inter-word spacings. Secondly, results demonstrated that inter-line spacings positively affect task-switching performance (between primary to secondary tasks). Taken together, we recommend W0_L100 as the optimal spacing, as it facilitates reading and walking performance. We formally evaluate W0_L100 in the following study.


The information displayed on OHMD should not occupy the entire screen [96, 97]. Previous research has suggested strategies for localising text content to a particular area on the screen, so as to balance the needs of reading and environmental awareness. Nine locations (top, middle, bottom X left, centre, right) were tested; middle-right [23], and bottom-centre [87] locations were found to be most suitable for text displays on OHMD in multitasking scenarios. In this study, we aim to find out how our text spacing approach compares with recommendations from previous research with different complexities in walking paths. Our research question is: Can the optimal text spacing interface from Study 2 outperform the middle-right and bottom-centre positions for mobile OHMD reading in walking paths with varying complexities?

7.1 Participants

We recruited 36 participants (17 males) between the ages of 19 and 34 (Mean = 23, SD = 3.00) from the university community. None of them participated in previous studies and had prior experience using OHMDs. All other participant details follow that of previous studies.

7.2 Design

The experiment used a two-factor, mixed factorial design. A between-subject factor Mobile Tasks has 2 levels (Simple, Complex) (see Fig. 2), and a within-subject factor Display Method has 3 levels as shown in Fig 8 (W0_L100, Bottom Center, Middle Right). We ensured that the number of words on screen was similar across all three levels (average of 96 words). Hence, the full design resulted in 3 conditions (3 display methods) per participant, counterbalanced using a balanced square design to avoid potential ordering effects. The same measures from previous studies were used.

Study 3 Conditions
Figure 8: Three types of text display method were evaluated in Study 3: (i) W0_L100) (ii) Bottom Center, (iii) Middle Right.

7.3 Results

Study 3 Results
Figure 9: Study 3 Results: Means and standard deviations (SD) of significant measures: (a) Reading Goodput, (b) Percentage of Preferred Walking Speed, (c) Switch-back Duration, (d) Switch-back Error Rate, (e) NASA-TLX workload for both simple and complex walking paths.

There was no significant interaction effect of Display Method x Mobile Tasks (p > 0.05) on all measurements. Also, there was no significant between-subject effect of Mobile Tasks (p > 0.05) on all measurements.

7.3.1 Primary reading performance: There was a significant effect of the Display Method (F2, 68 = 16.76, p < .01, $\eta ^{2}_{G}=0.04$) on Reading Goodput (see Fig. 9). Pairwise comparisons revealed that W0_L100 (M = 105.4 wpm) had a significantly higher Reading Goodput than both the bottom centre (M = 100.32 wpm, p < .05) and middle right (M = 94.19 wpm, p < .001). Also, there was a significant difference between the bottom centre and the middle right (p < .05). This suggests that participants read faster and more accurately with our text spacing interface than with positioning strategies. There was no significant effect of the Display Method (p > .05) on Reading Comprehension. This suggests that participants could comprehend the passages well with text spacing as well as the positioning strategies.

7.3.2 Secondary Task performance: There was no significant effect of the Display Method (p > 0.05) on Task Accuracy (see Fig. 9), suggesting that participants were similarly accurate when completing the secondary tasks with text spacing and positioning strategies. There was a significant effect of Display Method (F2, 68 = 3.97, p < .05, $\eta ^{2}_{G}=0.003$) on PPWS. Pairwise comparisons revealed that W0_L100 ($M=39.1\%$) had a significantly higher PPWS than the middle right ($M=37.69\%$, p < .001). But no significant difference was found between W0_L100 and bottom centre ($M=38.67\%$). This suggests that participants walked faster with the text spacing interface than the middle right position.

7.3.3 Overall Task switching performance: There was a significant effect of Display Method on both Switch-Back Duration (F2, 68 = 12.97, p < .001, $\eta ^{2}_{G}=0.06$) and Switch-Back Error Rate (F2, 68 = 3.87, p < .05, $\eta ^{2}_{G}=0.05$) (see Fig. 9). Pairwise comparisons revealed that W0_L100 (MSBD = 1.15s, $M_{SBER}=3.71\%$) had a significantly lower switch-back duration and reading error rate than the middle right (MSBD = 1.53s, $M_{SBER}=7.52\%$) (all p < .001) and bottom centre (MSBD = 1.52s, $M_{SBER}=7.66\%$) (all p = < .001) positions. However, no significant difference between the bottom centre and the middle right was found. This suggests that participants could switch back quickly with the text spacing interface to their primary task, and they could read more accurately with our text spacing interface upon resuming their primary task.

7.3.4 Subjective measures: There was a significant effect of Display Method (F2, 68 = 6.90, p < .05, $\eta ^{2}_{G}=0.05$) on perceived task workload (see Fig. 9). Pairwise comparisons revealed that W0_L100 (M = 50.8) had a significantly lower subject workload than the middle right (M = 57.13, p < .05). But no significant difference was found between W0_L100 and the bottom centre (M = 53.43), as well as the bottom centre and middle right. While Rzayev et al.’s study suggested that text read from the OHMD's bottom centre position can impose a lower subjective workload compared with other positions (i.e., middle right), this may be due to differences in task complexity; while Rzayev et al. utilised a simple walking scenario, our navigation task simulated real-world complex situations that naturally resulted in a higher workload.

For the simple walking path, eight participants preferred our text spacing interface over the two text positioning methods. Six participants preferred the bottom centre, while the remaining four preferred the middle right position. For the complex walking path, twelve participants preferred our text spacing interface over the two text positioning methods. Three participants preferred the bottom centre, while the remaining three preferred the middle right position. Between the bottom centre and middle right, eight participants preferred the former. This suggests that our text spacing strategy becomes more advantageous as the difficulty of the navigation path increases.

7.4 Discussion

7.4.1 Is the optimal text spacing interface identified from Study 2 better than the middle right and bottom centre positioning strategies for mobile OHMD reading?. In this study, a mixed design method was used, in which the same experiment was conducted on two groups with different walking path difficulties. The results obtained from both groups demonstrated a consistent pattern, providing strong evidence for the generalizability of the findings to a broader population and to varying levels of walking path difficulty. In previous studies [23, 87], positional approaches were not tested in scenarios displaying large amounts of text. While we do not claim to surpass these approaches, we were inspired by them in developing our own methods for displaying large chunks of text. We have identified three strategies for displaying such text, each of which maintains a similar total number of words to leave the same space for users to maintain their awareness of their surroundings. Although we kept the same remaining spaces on OHMD across the three Display Methods, the results revealed that our text spacing interface yields notably higher reading and walking performance over the two recommended position methods in both the simpler and the more complex walking paths. Participants’ preferences supported these because increasing inter-line space (W0_L100) “allow[ed] greater readability” (P5, P21). The spacing between lines also facilitated greater temporal awareness (P10, P25). P6 and P27 felt it was “easy [to continue] after switching between tasks”. Compared to the two positioning methods, participants felt that the text spacing strategy made it easy for them to locate where they had last stopped, requiring them to only “read a couple of words instead of glancing through sentences" (P7, P23). Additionally, participants’ preferences also suggested that our text spacing strategy becomes more beneficial as the navigation path's difficulty increases. Overall, results demonstrated that the spacing strategy identified in Study 2 could bring more benefits to the activity of mobile OHMD reading, perhaps more so than existing position-based layout strategies.

Eye Movement Cost Examples and Zig-Zag Pattern
Figure 10: Eye Movement Cost Examples and Zig-Zag Pattern. The reader's eye traces a Zig-Zag route when they read. As they scan from the top left to right, they form an imaginary horizontal line. As they continue from the top right to the next line, they create an imaginary diagonal line. This repeats until they arrive at the last word on the screen. (a) A text message with 38 words arranged in 3 lines within 1024x768 screen size. We assume the text width is 885 pixels and the default spacing is 4 pixels between lines with the font height to be 30 pixels. Increasing its (b) inter-word spacing and (c) inter-line spacing by 100 pixels increases the eye-movement cost by 150% and 0.4% respectively. This shows why the cost of increasing inter-word spacing is much more than that of increasing inter-line spacing for reading performance.
Examples for Design Implication
Figure 11: Examples for Design Implication. The inter-line spacing between the labels needs to be adjusted for a better viewing experience on OHMDs.


This study revealed two key insights: First, increasing spacings beyond the default spacings can significantly improve the mobile OHMD reading experience, especially during more complex navigation scenarios. Second, the effects of inter-word text spacings can differ significantly from inter-line spacings. In particular, Pearson's correlation test showed that inter-word spacings negatively correlate to reading performance while bearing no correlation to navigation and task-switching performance. In contrast, inter-line spacings positively correlate to reading performance, navigation, and task-switching performance.

In the next sections, we explore hypotheses and reasons for differences between inter-word and inter-line spacing. Our investigations involve three subtasks that determine mobile OHMD reading performance: 1) the reading task; 2) the navigation task, and 3) switching between the reading and navigation tasks.

8.1 The effect of spacing on reading performance.

Reading is a complex skill in which humans move their eyes three to four times every second as a result of visual and cognitive processing [16]. The EMMA eye-movement model [88] provides a theoretical foundation that relates eye movement with shifts in visual attention, and predicts that readers tend to take a longer time to encode individual visual objects when reading widely spaced text. Conversely, narrowly spaced text expedites the encoding and eye movement process, allowing for a more efficient reading experience. While this explains why increased overall spacing causes reading performance degradation, it does not explain the more nuanced varying effects between inter-word and inter-line spacing.

A possible explanation for this relates to the manner in which English text is generally read in a zigzag fashion [46]. While other reading styles have been studied (e.g., F-Pattern reading), we assume the general consensus that English is read in a Z-shape, from left to right and top to bottom (see Fig. 10). Considering this, inter-word spacing affects the reading of the words within the same line, while inter-line spacing affects the reading from the end of one line to the beginning of the next line. Since there are always fewer lines than the number of words, increasing inter-word space costs more overall eye movement in the overall reading experience than inter-line space. Fig. 10 illustrates a quantified example of this argument and explains why increasing inter-word spacing can have a significantly higher negative impact on users’ reading performance as compared to inter-line spacing.

8.2 The effect of spacing on navigation performance (or facilitating environmental awareness)

The fundamental advantage of OHMD-based reading lies in the direct augmentation of displayed information over the view of the physical environment [17, 43]. As the OHMD glass is transparent and see-through, participants are able to view the external environment via blank areas on the screen, i.e. areas that are not covered by text. This is a plausible explanation for why increased text spacing facilitated higher levels of environmental awareness in our participants, as indicated by their higher walking speed (PPWS) and lower mental workload.

However, this does not explain why only inter-line spacings improved navigation performance, while inter-word spacings failed to do so. We may consider the fact that each unit of inter-line spacing opens up a larger area of blank space than each unit of inter-word spacing (Fig. 10 (b) and Fig. 10 (c)) demonstrates this very point). Moreover, while line spacings open up an entire “row” of blank space, word spacings are more scattered and irregular across the screen, thus attenuating any positive effect word spacings may bring to navigation performance. These findings are supported by Pearson's correlation test, which showed that walking speed negatively correlates to mental workload, also consistent with previous studies [12, 49, 87].

8.3 The effect of spacing on facilitating task switching between reading and navigation

Spacings improved the task switching performance between reading and navigation, though inter-line spacing was much more effective than inter-word spacing in this regard. We flesh out possible reasons for this based on reading and visual search processes.

Firstly, task switching in our use case involves constant shifts in focus from text on-screen to the background environment, and vice-versa. When returning to text on-screen, participants undergo a visual search process in order to accurately and efficiently locate the last word they read (i.e. the visual target). All other words in the text function as non-target distractors. Previous research on crowding had suggested that visual targets become less visible when adjacent targets (i.e. distractors) are in close proximity[25, 27], which ultimately slows reading [33, 59, 73]. While the default spacing is not considered to be crowded during focused reading, it becomes “crowded” when both viewing clarity and cognitive resources are reduced in mobile multitasking scenarios, thus it results in suboptimal performance.

In the inter-word spacing case, while the number of distractors has been largely reduced, the organization of the information is less regular. This also increases the visual search difficulty.

As shown in Fig. 10, inter-line and inter-word spacings greatly reduce the number of distractors, though the former appears more visually organised (and less cluttered) than the latter. It is possible then, that inter-line spacing leads to faster and more accurate visual searching. In Study 2, the target-to-distractor visual search time and error rate for default spacing were 1.58s and 4.5% respectively. With inter-line spacing (100 pixels), visual search time was faster (0.74s), and the error rate was lower (1.4%). Previous research [84] demonstrated that by decreasing the target-to-distractor ratio, the visual search performance could be improved by 50%, which is close to what was observed in our experiment (53%-68%).

Our reasoning is also aligned with previous studies on multitasking which demonstrated that task-switching performance improves when the mental workload is lowered [9, 12, 29, 30, 49, 50, 61, 90, 93].

8.4 Applying our results to other OHMDs

The results obtained in this study apply to the NReal glasses in a specific font. Specifically, our study found that increasing the inter-line spacing to about 3 times the font height works well for the NReal light smart glasses, using a 30 pt font size. This can be used as a rough guideline, though caution should be exercised when applying the results to other contexts.

While we believe in the validity of the high-level conclusion: increasing inter-line spacing can significantly enhance the mobile reading experience on OHMDs, the exact amount of space to be allocated between the lines to achieve the optimal result can depend on many different factors, including the task, environment, device, and properties of the user. While our initial investigation has shown the advantages of inter-line spacing, it does not tell us how to determine the optimal spacing strategy across scenarios and contexts. To do so, we can either conduct many additional empirical studies involving all possible factors so that a more generalizable prediction model can be derived. Alternatively, we can train human agents using human modelling theories [63, 100] to test the results using computational simulations. The human's visual and oculomotor behaviour could be characterized and encoded as features for the smart agents in the reinforcement learning environment. We could place the agents in a finely-modulated environment, which simulates the visual inputs of a smart glass wearer. The agents could be regulated to have the same perception ability as real humans. Then we can run simulations to testify to the proposed method's validity with extended scenarios. The latter approach has the potential to more quickly and efficiently derive a generalizable prediction model which can be a promising avenue for future work.

Nevertheless, we believe our general finding remains sound, that inter-line spacing increased to a certain extent will significantly help mobile OHMD reading in more complex navigational scenarios, though exact spacing distances needed for each OHMD model and font size should be further tested and determined.


We explore the implications of our research and recommend methods for displaying text on OHMDs according to various design considerations.

Short text. : Our results do not apply to short text as our research assumes a substantial vertical dimension to the text body. Thus, text that can be displayed in 1 single line should use default spacings specified by previous research.

Text that involves multiple lines: Previous research has suggested breaking paragraphs into multiple lines and displaying them one by one. While this is effective, it affects the users’ ability to compare and review content on the screen. For example, in the case of learning, Ram et al. [77] suggested using a data persistence strategy so that information is retained on the screen for easy reviewing. In such cases, how information is properly laid out is an important design issue. One can imagine displaying a bullet list on OHMDs and, based on this research, leaving sufficient inter-line spacing to achieve a better on-the-go reading experience.

This result can also be applied to diagrams. In Fig. 11, the inter-line spacing between the labels needs to be adjusted for a better viewing experience on OHMDs.

10 Limitation & Future Work

Our research focuses on reading materials in English; however, text spacing strategies may apply differently to languages of different written scripts. Future work may explore how text spacing strategies can be generalised or translated to other languages for OHMD mobile reading scenarios.

In addition, while our mobile tasks were designed to reflect real-life navigation scenarios, our studies were conducted in the indoor environment instead of the outdoors. This design choice was intended to prevent visibility issues due to natural lighting as it potentially confounds OHMD reading performance [40]. Therefore, extending the proposed text spacing approach to outdoor scenarios will increase its ecological validity.

Furthermore, accurate eye-tracking on OHMD presents a challenge for researchers and developers [52]. Mobile eye-tracking should be able to account for environmental shaking and luminous variations, as well as determine the location on the OHMD where the user is looking [19, 69]. One potential solution is using a pupil core eye-tracker [6] as an add-on unit to OHMDs. While this approach has been effective in stationary environments, its accuracy decreases significantly during walking. Tobii's mobile eye-tracker [48] is another option, but it does not support the integration of OHMDs. Other companies, such as EyeTech Digital Systems [3], specialize in eye-tracking technology and offer a range of products and solutions, including mobile eye-trackers. However, it is worth noting that these solutions currently do not support the integration of OHMDs sufficiently well. In the context of our study on mobile reading with OHMDs, measuring the performance of task switching is important. However, the lack of eye-tracking data can be mitigated by having the user read the text aloud. Previous research has shown that reading aloud results in slower reading speeds than silent reading [26, 28], but this reduction is consistent across all conditions and does not significantly impact the final result.

Additionally, in the context of mobile reading on OHMDs, focal accommodation can be an important factor to consider. When using an OHMD, the display is positioned close to the eye, making it challenging for the eye to shift focus between the display and the surrounding environment. This can cause fatigue and discomfort for the user, as well as hinder their ability to read effectively [14, 39, 47]. Since the text we use is presented in 2D, the accommodation-vergence reflex [14], which is primarily associated with viewing 3D objects, less applies in our case. Yet our study design took these factors into account. We instructed participants to ensure that they could see the text clearly during the training and also trained them to adjust the focus of the OHMD. Additionally, we controlled other factors that may affect focal accommodation, such as the font size and contrast of the text, and the lighting in the environment [10, 11]. Considering these factors, we ensure participants can read comfortably and effectively after training.

Lastly, although our investigations focus on the spacing between words and lines in text. In reality, spacing is applied to not only text but also graphical objects. Thus, more research is needed to explore how text spacing can be optimised for a more effective OHMD mobile reading. These findings could inform the design of future intelligent adaptive text spacing systems for OHMD mobile reading scenarios.


In this work, we systematically investigated the possibilities and benefits of text spacing as applied to the OHMD mobile reading context. It is easy to assume that text should be shown on OHMD as text displayed on mobile or desktop screens, but our research shows that this is not the case, especially during more complex navigation scenarios. We have developed a text spacing strategy that was proven to work for OHMD mobile reading. Our paper takes a significant step forward in understanding how to design text presentations for OHMD mobile reading and we highlight that text spacings are, in fact, important. We further break down the idea of “text spacings” into important variables, such as dimensionality (inter-word or inter-line) and distance (50 pixels, 100 pixels, etc). These are key factors that influence the outcome of mobile reading. Finally, insights gained from this study can be extended to other explorations in the information acquisition of OHMD in multitasking scenarios.


This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-RP-2020-016). It is also supported in part by the Ministry of Education, Singapore, under its MOE Academic Research Fund Tier 2 programme (MOE-T2EP20221-0010), and by a research grant #22-5913-A0001 from the Ministry of Education of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the Ministry of Education, Singapore.


  • 2022. Acereader. Accessed: 2022-09-15.
  • 2022. Apple iPad mini. Accessed: 2022-09-15.
  • 2022. EyeTech Digital Systems. Accessed: 2022-12-12.
  • 2022. The Flesch Reading Ease Readability Formula. Accessed: 2022-09-15.
  • 2022. Nreal Light. Accessed: 2022-09-15.
  • 2022. Pupil Core Eye-tracker. Accessed: 2022-12-12.
  • 2022. Space (punctuation). Accessed: 2022-09-15.
  • 2022. Typography. Accessed: 2022-09-15.
  • Piotr D. Adamczyk and Brian P. Bailey. 2004. If Not Now, When? The Effects of Interruption at Different Moments within Task Execution. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vienna, Austria) (CHI ’04). Association for Computing Machinery, New York, NY, USA, 271–278.
  • Mohammed Safayet Arefin, Nate Phillips, Alexander Plopski, Joseph L Gabbard, and J Edward Swan. 2020. Impact of ar display context switching and focal distance switching on human performance: Replication on an ar haploscope. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 571–572.
  • Mohammed Safayet Arefin, Nate Phillips, Alexander Plopski, Joseph L Gabbard, and J Edward Swan. 2022. The Effect of Context Switching, Focal Switching Distance, Binocular and Monocular Viewing, and Transient Focal Blur on Human Performance in Optical See-Through Augmented Reality. IEEE Transactions on Visualization and Computer Graphics 28, 5(2022), 2014–2025.
  • Brian P. Bailey and Shamsi T. Iqbal. 2008. Understanding Changes in Mental Workload during Execution of Goal-Directed Tasks and Its Application for Interruption Management. ACM Trans. Comput.-Hum. Interact. 14, 4, Article 21 (jan 2008), 28 pages.
  • Leon Barnard, Ji Soo Yi, Julie A. Jacko, and Andrew Sears. 2007. Capturing the Effects of Context on Human Performance in Mobile Computing Systems. 11, 2 (jan 2007), 81–96.
  • Anil Ufuk Batmaz, Mayra Donaji Barrera Machuca, Junwei Sun, and Wolfgang Stuerzlinger. 2022. The Effect of the Vergence-Accommodation Conflict on Virtual Hand Pointing in Immersive Displays. In CHI Conference on Human Factors in Computing Systems. 1–15.
  • Michael L. Bernard, Barbara S. Chaparro, Melissa M. Mills, and Charles G. Halcomb. 2003. Comparing the effects of text size and format on the readibility of computer-displayed Times New Roman and Arial text. International Journal of Human-Computer Studies 59, 6 (2003), 823–835.
  • Klinton Bicknell, Roger Levy, and Keith Rayner. 2020. Ongoing Cognitive Processing Influences Precise Eye-Movement Targets in Reading. Psychological Science 31, 4 (2020), 351–362. PMID: 32105193.
  • Manuel Birlo, P.J. Eddie Edwards, Matthew Clarkson, and Danail Stoyanov. 2022. Utility of optical see-through head mounted displays in augmented reality-assisted surgery: A systematic review. Medical Image Analysis 77 (2022), 102361.
  • Sally Blackmore-Wright, Mark A. Georgeson, and Stephen J. Anderson. 2013. Enhanced Text Spacing Improves Reading Performance in Individuals with Macular Disease. PLOS ONE 8, 11 (11 2013), null.
  • Pieter Blignaut and Daniël Wium. 2014. Eye-tracking data quality as affected by ethnicity and experimental design. Behavior research methods 46, 1 (2014), 67–80.
  • Marc Brysbaert. 2019. How many words do we read per minute? A review and meta-analysis of reading rate. Journal of Memory and Language 109 (2019), 104047.
  • Aurélie Calabrèse, Jean-Baptiste Bernard, Louis Hoffart, Géraldine Faure, Fatiha Barouch, John Conrath, and Eric Castet. 2010. Small Effect of Interline Spacing on Maximal Reading Speed in Low-Vision Patients with Central Field Loss Irrespective of Scotoma Size. Investigative Ophthalmology & Visual Science 51, 2 (02 2010), 1247–1254.
  • Jian Chen, Pardha S Pyla, and Doug A Bowman. 2004. Testbed evaluation of navigation and text display techniques in an information-rich virtual environment. In IEEE Virtual Reality 2004. IEEE, 181–289.
  • Soon Hau Chua, Simon T. Perrault, Denys J. C. Matthies, and Shengdong Zhao. 2016. Positioning Glass: Investigating Display Positions of Monocular Optical See-Through Head-Mounted Display. In Proceedings of the Fourth International Symposium on Chinese CHI (San Jose, USA) (ChineseCHI2016). Association for Computing Machinery, New York, NY, USA, Article 1, 6 pages.
  • Susana Chung. 2004. Reading Speed Benefits from Increased Vertical Word Spacing in Normal Peripheral Vision. Optometry and vision science : official publication of the American Academy of Optometry 81 (08 2004), 525–35.
  • Susana T. L. Chung, Roger W. Li, and Dennis M. Levi. 2007. Crowding between first- and second-order letter stimuli in normal foveal and peripheral vision. Journal of Vision 7, 2 (03 2007), 10–10. arXiv:
  • Massimo Ciuffo, Jane Myers, Massimo Ingrassia, Antonio Milanese, Maria Venuti, Ausilia Alquino, Alice Baradello, Giacomo Stella, and Antonella Gagliano. 2017. How fast can we read in the mind? Developmental trajectories of silent reading fluency. Reading and Writing 30, 8 (2017), 1667–1686.
  • D. Cline, H.W. Hofstetter, and J.R. Griffin. 1989. Dictionary of Visual Science. Chilton.
  • Ray Collins. 1961. The comprehension of prose materials by college freshmen when read silently and when read aloud. The Journal of Educational Research 55, 2 (1961), 79–82.
  • Edward B. Cutrell, Mary Czerwinski, and Eric Horvitz. 2000. Effects of Instant Messaging Interruptions on Computing Tasks. In CHI ’00 Extended Abstracts on Human Factors in Computing Systems (The Hague, The Netherlands) (CHI EA ’00). Association for Computing Machinery, New York, NY, USA, 99–100.
  • Mary Czerwinski, Edward Cutrell, and Eric Horvitz. 2000. Instant Messaging: Effects of Relevance and Timing. (11 2000).
  • Saverio Debernardis, Michele Fiorentino, Michele Gattullo, Giuseppe Monno, and Antonio Emmanuele Uva. 2014. Text Readability in Head-Worn Displays: Color and Style Optimization in Video versus Optical See-Through Devices. IEEE Transactions on Visualization and Computer Graphics 20, 1(2014), 125–139.
  • Andrew Dillon, John Richardson, and Cliff McKnight. 1990. The effects of display size and text splitting on reading lengthy text from screen. Behaviour & Information Technology 9, 3 (1990), 215–227.
  • Jonathan Dobres, Benjamin Wolfe, Nadine Chahine, and Bryan Reimer. 2018. The effects of visual crowding, text size, and positional uncertainty on text legibility at a glance. Applied Ergonomics 70(2018), 240–246.
  • Sanika Doolani, Callen Wessels, Varun Kanal, Christos Sevastopoulos, Ashish Jaiswal, Harish Nambiappan, and Fillia Makedon. 2020. A review of extended reality (xr) technologies for manufacturing training. Technologies 8, 4 (2020), 77.
  • Brian L Due. 2014. The future of smart glasses: An essay about challenges and possibilities with smart glasses. Vol. 1. Centre of Interaction Research and Communication Design, University of ….
  • Doaa Farouk Badawy Eldesouky. 2013. Visual hierarchy and mind motion in advertising design. Journal of Arts and Humanities 2, 2 (2013), 148–162.
  • Julie Epelboim, James R. Booth, and Robert M. Steinman. 1994. Reading unspaced text: Implications for theories of reading eye movements. Vision Research 34, 13 (1994), 1735–1766.
  • Shogo Fukushima, Takeo Hamada, and Ari Hautasaari. 2020. Comparing World and Screen Coordinate Systems in Optical See-Through Head-Mounted Displays for Text Readability while Walking. In 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 649–658.
  • Joseph L Gabbard, Divya Gupta Mehra, and J Edward Swan. 2018. Effects of AR display context switching and focal distance switching on human performance. IEEE transactions on visualization and computer graphics 25, 6(2018), 2228–2241.
  • Joseph L. Gabbard, J. Edward Swan, and Deborah Hix. 2006. The Effects of Text Drawing Styles, Background Textures, and Natural Lighting on Text Legibility in Outdoor Augmented Reality. Presence 15, 1 (2006), 16–32.
  • Joseph L. Gabbard, J. Edward Swan, Deborah Hix, Si-Jung Kim, and Greg Fitch. 2007. Active Text Drawing Styles for Outdoor Augmented Reality: A User-Based Study and Design Implications. In 2007 IEEE Virtual Reality Conference. 35–42.
  • Jessica Galliussi, Luciano Perondi, Giuseppe Chia, Walter Gerbino, and Paolo Bernardis. 2020. Inter-letter spacing, inter-word spacing, and font with dyslexia-friendly features: testing text readability in people with and without dyslexia. Annals of dyslexia 70 (03 2020).
  • Debjyoti Ghosh, Pin Sym Foong, Shengdong Zhao, Can Liu, Nuwan Janaka, and Vinitha Erusu. 2020. EYEditor: Towards On-the-Go Heads-Up Text Editing Using Voice and Manual Input. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13.
  • Sandra G. Hart and Lowell E. Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Human Mental Workload, Peter A. Hancock and Najmedin Meshkati (Eds.). Advances in Psychology, Vol. 52. North-Holland, 139–183.
  • Ania Hernandez and Marc L Resnick. 2013. Placement of call to action buttons for higher website conversion and acquisition: An eye tracking study. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 57. SAGE Publications Sage CA: Los Angeles, CA, 1042–1046.
  • Bruce Hillard, Jocelyn Amarego, and Tanya McGill. 2016. Optimising visual layout for training and learning technologies. (2016).
  • David M Hoffman, Ahna R Girshick, Kurt Akeley, and Martin S Banks. 2008. Vergence–accommodation conflicts hinder visual performance and cause visual fatigue. Journal of vision 8, 3 (2008), 33–33.
  • Andrew Housholder, Jonathan Reaban, Aira Peregrino, Georgia Votta, and Tauheed Khan Mohd. 2021. Evaluating Accuracy of the Tobii Eye Tracker 5. In International Conference on Intelligent Human Computer Interaction. Springer, 379–390.
  • Shamsi T. Iqbal and Brian P. Bailey. 2005. Investigating the Effectiveness of Mental Workload as a Predictor of Opportune Moments for Interruption. In CHI ’05 Extended Abstracts on Human Factors in Computing Systems (Portland, OR, USA) (CHI EA ’05). Association for Computing Machinery, New York, NY, USA, 1489–1492.
  • Shamsi T. Iqbal and Brian P. Bailey. 2006. Leveraging Characteristics of Task Structure to Predict the Cost of Interruption. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(Montréal, Québec, Canada) (CHI ’06). Association for Computing Machinery, New York, NY, USA, 741–750.
  • Jacek Jankowski, Krystian Samp, Izabela Irzynska, Marek Jozwowicz, and Stefan Decker. 2010. Integrating Text with Video and 3D Graphics: The Effects of Text Drawing Styles on Text Readability. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Atlanta, Georgia, USA) (CHI ’10). Association for Computing Machinery, New York, NY, USA, 1321–1330.
  • Sebastian Kapp, Michael Barz, Sergey Mukhametov, Daniel Sonntag, and Jochen Kuhn. 2021. ARETT: augmented reality eye tracking toolkit for head mounted displays. Sensors 21, 6 (2021), 2234.
  • Elisa Maria Klose, Nils Adrian Mack, Jens Hegenberg, and Ludger Schmidt. 2019. Text Presentation for Augmented Reality Applications in Dual-Task Situations. In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). 636–644.
  • Pin-Sung Ku, Yu-Chih Lin, Yi-Hao Peng, and Mike Y. Chen. 2019. PeriText: Utilizing Peripheral Vision for Reading Text on Augmented Reality Smart Glasses. In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). 630–635.
  • Richard L Lewis, Michael Shvartsman, and Satinder Singh. 2013. The adaptive nature of eye movements in linguistic tasks: How payoff and architecture shape speed-accuracy trade-offs. Topics in cognitive science 5, 3 (2013), 581–610.
  • A. Leykin and M. Tuceryan. 2004. Automatic determination of text readability over textured backgrounds for augmented reality systems. In Third IEEE and ACM International Symposium on Mixed and Augmented Reality. 224–230.
  • Bob W Lord and Ray Velez. 2013. Converge: transforming business at the intersection of marketing and technology. John Wiley & Sons.
  • Andrés Lucero and Akos Vetek. 2014. NotifEye: Using Interactive Glasses to Deal with Notifications While Walking in Public. In Proceedings of the 11th Conference on Advances in Computer Entertainment Technology (Funchal, Portugal) (ACE ’14). Association for Computing Machinery, New York, NY, USA, Article 17, 10 pages.
  • Marialuisa Martelli, Gloria Di Filippo, Donatella Spinelli, and Pierluigi Zoccolotti. 2009. Crowding, reading, and developmental dyslexia. Journal of Vision 9, 4 (04 2009), 14–14. arXiv:
  • Yuki Matsuura, Tsutomu Terada, Tomohiro Aoki, Susumu Sonoda, Naoya Isoyama, and Masahiko Tsukamoto. 2019. Readability and Legibility of Fonts Considering Shakiness of Head Mounted Displays. In Proceedings of the 23rd International Symposium on Wearable Computers (London, United Kingdom) (ISWC ’19). Association for Computing Machinery, New York, NY, USA, 150–159.
  • Daniel C. McFarlane. 2002. Comparison of Four Primary Methods for Coordinating the Interruption of People in Human-Computer Interaction. Human–Computer Interaction 17, 1 (2002), 63–139.
  • Carol Bergfeld Mills and Linda J Weldon. 1987. Reading text from computer screens. ACM Computing Surveys (CSUR) 19, 4 (1987), 329–357.
  • Roderick Murray-Smith, Antti Oulasvirta, Andrew Howes, Jörg Müller, Aleksi Ikkala, Miroslav Bachinski, Arthur Fleig, Florian Fischer, and Markus Klar. 2022. What simulation can do for HCI research. Interactions 29, 6 (2022), 48–53.
  • Terhi Mustonen, Mikko Berg, Jyrki Kaistinen, Takashi Kawai, and Jukka Häkkinen. 2013. Visual Task Performance Using a Monocular See-Through Head-Mounted Display (HMD) While Walking. Journal of experimental psychology. Applied 19 (11 2013).
  • Jakob Nielsen. 2006. "F-Shaped Pattern For Reading Web Content," Jakob Nielsen's Alertbox. http://www. useit. com/alertbox/reading_pattern. html (2006).
  • Eyal Ofek, Shamsi T Iqbal, and Karin Strauss. 2013. Reducing disruption from subtle information delivery during a conversation: mode and bandwidth investigation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 3111–3120.
  • Jason Orlosky, Kiyoshi Kiyokawa, and Haruo Takemura. 2013. Dynamic Text Management for See-through Wearable and Heads-up Display Systems. In Proceedings of the 2013 International Conference on Intelligent User Interfaces (Santa Monica, California, USA) (IUI ’13). Association for Computing Machinery, New York, NY, USA, 363–370.
  • Jason Orlosky, Kiyoshi Kiyokawa, and Haruo Takemura. 2014. Managing Mobile Text in Head Mounted Displays: Studies on Visual Preference and Text Placement. SIGMOBILE Mob. Comput. Commun. Rev. 18, 2 (jun 2014), 20–31.
  • Rie Osawa and Susumu Shirayama. 2018. A method to compensate head movements for mobile eye tracker using invisible markers. Journal of Eye Movement Research 11, 1 (2018).
  • Antti Oulasvirta, Sakari Tamminen, Virpi Roto, and Jaana Kuorelahti. 2005. Interaction in 4-Second Bursts: The Fragmented Nature of Attentional Resources in Mobile HCI. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Portland, Oregon, USA) (CHI ’05). Association for Computing Machinery, New York, NY, USA, 919–928.
  • Harold Pashler. 1994. Dual-task interference in simple tasks: data and theory.Psychological bulletin 116 2 (1994), 220–44.
  • P.David Pearson and Margaret C. Gallagher. 1983. The instruction of reading comprehension. Contemporary Educational Psychology 8, 3 (1983), 317–344.
  • Denis Pelli, Sarah Waugh, Marialuisa Martelli, Sebastian Crutch, Silvia Primativo, Keir Yong, Marjorie Rhodes, Kathryn Yee, Xiuyun Wu, Hannes Famira, and Hörmet Yiltiz. 2016. A clinical test for visual crowding. F1000Research 5 (01 2016).
  • Manuel Perea and Joana Acha. 2009. Space information is important for reading. Vision Research 49, 15 (2009), 1994–2000.
  • Alexander Pollatsek and Keith Rayner. 1982. Eye movement control in reading: The role of word boundaries.Journal of Experimental Psychology: Human Perception and Performance 8, 6(1982), 817.
  • Eric D Ragan, Alex Endert, Doug A Bowman, and Francis Quek. 2012. How spatial layout, interactivity, and persistent visibility affect learning with large displays. In Proceedings of the International Working Conference on Advanced Visual Interfaces. 91–98.
  • Ashwin Ram and Shengdong Zhao. 2021. LSVP: Towards Effective On-the-Go Video Learning Using Optical Head-Mounted Displays. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 1, Article 30 (mar 2021), 27 pages.
  • Keith Rayner. 1977. Visual attention in reading: Eye movements reflect cognitive processes. Memory & cognition 5, 4 (1977), 443–448.
  • Keith Rayner. 1998. Eye movements in reading and information processing: 20 years of research.Psychological bulletin 124, 3 (1998), 372.
  • Keith Rayner, Martin H. Fischer, and Alexander Pollatsek. 1998. Unspaced text interferes with both word identification and eye movement control. Vision Research 38, 8 (1998), 1129–1144.
  • Keith Rayner and Alexander Pollatsek. 1996. Reading unspaced text is not easy: Comments on the implications of Epelboim et al.’s (1994) study for models of eye movement control in reading. Vision Research 36, 3 (1996), 461–465.
  • Keith Rayner, Alexander Pollatsek, Jane Ashby, and Charles Clifton Jr. 2012. Psychology of reading. Psychology Press.
  • Keith Rayner, Jinmian Yang, Monica Castelhano, and Simon Liversedge. 2010. Eye Movements of Older and Younger Readers When Reading Disappearing Text. Psychology and aging 26 (12 2010), 214–23.
  • Lavanya Reddy and Rufin VanRullen. 2007. Spacing affects some but not all visual searches: Implications for theories of attention and crowding. Journal of Vision 7, 2 (02 2007), 3–3. arXiv:
  • Alexander I Rudnicky and Paul A Kolers. 1984. Size and case of type as stimuli in reading.Journal of Experimental Psychology: Human Perception and Performance 10, 2(1984), 231.
  • André A. Rupp, Tracy Ferne, and Hyeran Choi. 2006. How assessing reading comprehension with multiple-choice questions shapes the construct: a cognitive processing perspective. Language Testing 23, 4 (2006), 441–474.
  • Rufat Rzayev, Paweł W. Woźniak, Tilman Dingler, and Niels Henze. 2018. Reading on Smart Glasses: The Effect of Text Position, Presentation Type and Walking(CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–9.
  • Dario D Salvucci. 2001. An integrated model of eye movements and visual encoding. Cognitive Systems Research 1, 4 (2001), 201–220.
  • Shardul Sapkota, Ashwin Ram, and Shengdong Zhao. 2021. Ubiquitous Interactions for Heads-Up Computing: Understanding Users’ Preferences for Subtle Interaction Techniques in Everyday Settings. In Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction. 1–15.
  • Angela Sasse and Daniel McFarlane. 1970. Coordinating the Interruption of People in Human-Computer Interaction. (02 1970).
  • Bastian Schildbach and Enrico Rukzio. 2010. Investigating Selection and Reading Performance on a Mobile Phone While Walking. In Proceedings of the 12th International Conference on Human Computer Interaction with Mobile Devices and Services (Lisbon, Portugal) (MobileHCI ’10). Association for Computing Machinery, New York, NY, USA, 93–102.
  • Timothy Slattery and Keith Rayner. 2013. Effects of intraword and interword spacing on eye movements during reading: Exploring the optimal use of space in a line of text. Attention, perception & psychophysics 75 (05 2013).
  • Cheri Speier, Joseph Valacich, and Iris Vessey. 1997. The effects of task interruption and information presentation on individual decision making. 21–36.
  • Ayoung Suh and Jane Prophet. 2018. The state of immersive technology research: A literature analysis. Computers in Human Behavior 86 (2018), 77–90.
  • Kohei Tanaka, Yasue Kishino, Masakazu Miyamae, Tsutomu Terada, and Shojiro Nishio. 2008. An information layout method for an optical see-through head mounted display focusing on the viewability. In 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality. 139–142.
  • Anne M. Treisman and Garry Gelade. 1980. A feature-integration theory of attention. Cognitive Psychology 12, 1 (1980), 97–136.
  • Thomas S. Tullis. 1997. Chapter 23 - Screen Design. In Handbook of Human-Computer Interaction (Second Edition) (second edition ed.), Marting G. Helander, Thomas K. Landauer, and Prasad V. Prabhu (Eds.). North-Holland, Amsterdam, 503–531.
  • Jacob O. Wobbrock, Leah Findlater, Darren Gergle, and James J. Higgins. 2011. The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only Anova Procedures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vancouver, BC, Canada) (CHI ’11). Association for Computing Machinery, New York, NY, USA, 143–146.
  • Alexander Woodham, Mark Billinghurst, and William S. Helton. 2016. Climbing With a Head-Mounted Display: Dual-Task Costs. Human Factors 58, 3 (2016), 452–461. arXiv: 26865416.
  • Mesut Yang, Micah Carroll, and Anca Dragan. 2022. Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaboration. arXiv preprint arXiv:2211.01602(2022).

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