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Investigating age-related differences in spatial presence formation and maintenance in virtual reality

(Georgia Institute of Technology, 2019-04-02) McGlynn, Sean A.

Virtual reality has numerous applications with the potential to support physical, cognitive, and socio-emotional well-being across a range of users. The effectiveness of these applications in achieving desirable outcomes (e.g., transfer of training, enjoyment, treatment efficacy) has been shown to depend on the extent that the user experiences a sense of being physically located in the virtual environment. This 'sense of being' is termed spatial presence. Research on this concept has primarily focused on the effect that the objective immersiveness of the system (e.g., screen resolution, field of view, audio quality) has on the level of spatial presence that users experience in the virtual environment. The goal of this dissertation was to better understand the components of the full spatial presence process (i.e., formation and maintenance), validate measurement methods for capturing within-experience changes in spatial presence formation and maintenance, changes in spatial presence levels over time, and the cognitive abilities that influence spatial presence formation and maintenance. 25 younger and 25 older adults participated in virtual reality experiences over the course of three days. Age was used as a proxy for changes in cognitive abilities. Additionally, measures of specific attentional abilities were administered as well as existing and novel measures of spatial presence during and after the virtual reality. The primary findings of this dissertation are as follows: 1) In general, there was little evidence of age-related or time-related differences in spatial presence, 2) Presence formation occurred rapidly, 3) Participants experienced high levels of spatial presence, 4) Participants maintained spatial presence in the virtual environment for the majority of their sessions, 5) Disturbances in presence were easily recovered from, 6) Methods of measuring within-experience fluctuations in presence were validated, with some methodological caveats. These findings are informative to spatial presence theory, future research, and measurement and have practical contributions for designers of virtual reality applications, experiences, and systems.
Understanding dimensions of trust between older adults and human or robot care providers

(Georgia Institute of Technology, 2017-08-09) Stuck, Rachel Elizabeth

As the number of older adults in the US increases, the need for care providers, both personal care attendants and robots, for older adults will also increase. Understanding how to develop trust in the relationship between older adults and care providers is important for maintaining a dyad that works effectively. Trust has been studied in several contexts, but not specifically with older adults and care providers in personal care tasks. To gain knowledge of how dimensions of trust in human-human and human-robot dyads interrelate we conducted semi-structured interviews and administered questionnaires to: (1) gain insight into the factors that influence older adults' trust in human and robotic care providers and (2) clarify how the factors that influence trust differentiate for human-human versus human-robot relationships in the context of older adult and care providers. The older adults interviewed in this study discussed three main categories of factors that they perceived as supporting trust in human and robot care providers: professional skills, personal traits, and communication. For both the human and robot care provider, older adults discussed previously identified factors as well as emergent themes from this context. For the human care provider, previously identified themes such as general capability, reliability, benevolence, and values were discussed frequently. However, new themes such as the human care providers attitude towards the task and manner of dress that emerged as important to the older adult. For the robot care provider, older adults discussed previously identified aspects such as general capability, predictability, and reliability, as well as new themes within human-robot trust such as benevolence of the robot, the material or texture of the robot, and whether or not the robot had similar values. In addition, this study found that personal traits were mentioned more frequently for the human than for the robot. While previous models of trust encompass many of the factors that support trust within this context, they are not sufficient. Within these personal care tasks, older adults emphasized not only the importance of the task being performed properly, but also emphasized personal traits and characteristics influencing trust albeit less for the robot than human. Participants also frequently discussed communication and how the care providers could use communication to support trust. These findings expand what we know about trust within the older adult-care provider context and can be used to advance the training of human care providers and the design of home robots to help improve the lives of older adults.
Assessing influences on the medication management strategies of older adults with hypertension

(Georgia Institute of Technology, 2017-08-09) Blocker, Kenneth

Many older adults are living with at least one chronic disease and must adhere to prescribed medications to control the impact of these diseases. Most common is hypertension, a mostly asymptomatic disease in which one’s blood pressure is elevated in comparison to healthy levels. Thus, there may not be symptoms to remind one to take their daily medication and, as older adults may experience declines in some forms of memory as they age, these individuals may face challenges in properly adhering to their prescribed antihypertensive medications. Multiple factors (e.g., illness representations, goals, control beliefs) influence the strategies older adults employ to ensure the successful management of their medication, helping to control their blood pressure. However, more research is needed to better understand the factors that influence the utilization and effectiveness of these strategies. The goal of the current study was to understand how older adults approached the management of their antihypertensive medication as well as the factors that influence this management. A semi-structured interview was performed to obtain in-depth information regarding the medication management strategies and opinions of individuals aged 65-85 who have been diagnosed with hypertension. Participants, on average, expressed using, on average, approximately 4 strategies in their medication management routines. The association strategy was found to be the most commonly endorsed as well as perceived as the most effective. In addition to strategy use, misconceptions regarding individuals’ knowledge of the disease, as well as incongruities between self-reported adherence and participants’ perceived medication management ability, were evident in the interview data. These findings inform our theoretical understanding of how older adults approach managing their antihypertensive medication as well as what might be contributing to the difficulties that individuals diagnosed with the disease have experienced regarding its management. Additionally, these findings inform the design of more effective tools geared toward improving and maintaining antihypertensive medication adherence (e.g., interventions, hardware/software applications).
Age-related differences in medication risk taking

(Georgia Institute of Technology, 2017-07-13) Chong, Wing Lam

Prior studies on older adults’ risk taking have paid little attention to the healthcare domain. The current study examined age-related differences in medication risk taking. Participants were 36 English speaking younger adults (55.6% females) between the ages of 19 and 26 (M = 20.94, SD = 1.55), and 35 English speaking older adults (60.0% females) between the ages of 67 and 80 (M = 72.34, SD = 3.09). We asked them to choose between hypothetical medications that differed in probabilities and outcomes of treatment success. To investigate the effects of risk-disadvantageous versus risk-neutral versus risk-advantageous situations, participants chose between a risky option and a sure option that had a higher expected value (risk-disadvantageous), between a risky option and a sure option that had equal expected values (risk-neutral), and between a risky option and a sure option that had a lower expected value (risk-advantageous). Overall, older adults were more risk averse. Older adults also showed a smaller increase in risk-taking tendency across risk-disadvantageous, risk-neutral, and risk-advantageous situations compared to younger adults, consistent with the idea that younger adults are more likely to use verbatim processing than older adults in making decisions (Peters et al., 2007; Reyna & Brainerd, 2011). Further examination of individual participant’s medication risky choices revealed that younger and older adults could be essentially classified into three groups: younger adults who were sensitive to expected value differences between options, older adults who took fewer risks than did younger adults but were sensitive to expected value differences, and older adults who were extremely risk averse and exhibited no sensitivity to expected value differences (54.29% of the older adult sample). Post-hoc exploratory analyses found that a variety of individual difference measures (i.e., education, perceived health, numeracy, health literacy, global cognitive ability, perceived severity of sickness) did not differentiate sensitive and insensitive older adults. This could indicate that other variables should be considered as an explanation for the large inter-individual variability in sensitivity among older adults. These findings emphasize the importance of designing decision aids to encourage older adults to take more (fewer) risks when risk taking is more (less) beneficial, and point to the need for improving the communication of outcome and probability information in medication risky decisions to older adults.
Understanding multiple task coordination in a complex healthcare environment

(Georgia Institute of Technology, 2017-04-07) Barg-Walkow, Laura Hillary

Understanding multiple task coordination is important in complex life-critical environments. In healthcare, for example, many situations occur in which there are multiple tasks and limited resources for addressing all tasks at the same time. Emergency departments in particular are complex, interruption-driven environments. In many cases, physicians in emergency departments do not complete a single task in isolation. Decisions regarding what tasks to do, and when to do them, can affect performance (e.g., time, accuracy, patient safety). Additionally, some task factors (e.g., priority, difficulty) can drive task coordination behaviors. Characteristics of interruptions, such as frequencies and types, in emergency departments have been studied, but there has been little research on how physicians schedule and manage multiple tasks. The purpose of this research was to investigate multiple task coordination by emergency physicians to understand strategies for task completion, strategies for task scheduling, and management of interruptions. I conducted two studies to understand how emergency physicians coordinate multiple tasks. The goal of the first study was to understand task scheduling decisions by physicians in emergency departments through a modeling approach. This study consisted of an online questionnaire conducted with 170 emergency physicians (120 attending and 50 resident physicians). There were two primary research aims: to understand (1) task scheduling decisions in a multiple task context, and (2) how task scheduling decisions varied across experience level. Attending physicians’ task scheduling decisions aligned more with a parsimonious one-reason rule, where priority was the only factor that influenced decisions. Alternatively, resident physicians’ decisions were not driven by priority, but rather were influenced by difficulty, salience, and engagement. This indicates that physicians may be differentially weighting different cues as they make decisions about how to order tasks, and provides insights for how to support decision making as these strategies are learned. The goal of the second study was to understand how multiple task demands are managed and coordinated by physicians in emergency departments. This study consisted of questionnaires and interviews with 30 emergency physicians (15 attending and 15 resident physicians). There were three primary research aims: to understand (1) strategies used for multiple task coordination, including both completion and scheduling strategies; (2) how interruptions were conceptualized and coordinated, and (3) how multiple task coordination varied across experience level. I identified and hierarchically categorized a broad set of strategies for task completion, and determined that these strategies did not change with experience. For task scheduling, I confirmed that previously-identified factors drove task scheduling. I also better defined factors (e.g., splitting priority into urgency and criticality) and identified additional factors (e.g., time and its subcomponents, interpersonal skills). Although there were common task scheduling factors mentioned by all 30 participants (e.g., priority, time), other factors were identified more often by attending physicians than resident physicians (e.g., interpersonal skills). I also found that conceptualizations of interruptions in this environment did not significantly differ from existing definitions; however, participants discussed the need to clarify between positive and negative interruptions. Overall, this research provided insights into task coordination in a complex, interruption-driven healthcare context. In this work, I investigated strategies for task scheduling, including further evaluating known factors (e.g., priority) and identifying additional factors (e.g., time) that drive task scheduling decisions. I combined insights from both quantitative and qualitative methods to evaluate hypothesis-driven models for task scheduling. In this case, findings from Study 1 indicated that a one-reason priority-only model best captured attending physicians’ task scheduling decisions and a multi-attribute model best captured resident physicians’ task scheduling decisions; however, findings from Study 2 indicated a rich set of factors that are used by emergency physicians beyond those factors in the models. This indicates more parameters should be included in modeling studies to better evaluate task scheduling decisions. The results of this dissertation have implications for improving training and evaluation of physicians as well as designing tools to support multiple task coordination.
How people interpret and react to everyday automation issues

(Georgia Institute of Technology, 2016-05-19) Preusse, Kimberly C.

Automation is frequently used in everyday life. However, automation can err and thereby complicate human-automation interactions. Current human-automation literature has investigated cues (e.g., frequency) people use to know that an automation issue has occurred (e.g., Itoh, Abe, & Tanaka, 1999) but has lacked investigation into the interpretation people have of the issues. Additionally, there is a need to understand how people respond to automation issues when the constraints of an experimental setting are lifted and response options can range beyond continuing to use the automation or ceasing use of the automation (e.g., Dzindolet, Peterson, Pomranky, Pierce, & Beck, 2003). The present study utilized two different interview methods to qualitatively examine the cues people used to interpret an automation issue, their interpretation of the issue, the reasons they use in deciding how to respond, and the response strategies they have for an automation issue. Results demonstrate the generalizability of cues currently in the human-automation literature and reveal previously undocumented cues (e.g., measurement comparison). Further, users do not always interpret issues causally, and may instead interpret issues generally or may understand where the issue occurred but not why (i.e., specifically). The cues people use to understand automation issues differ on if they interpreted an issue generally, specifically, or causally. Results also documented the additional response strategies of (1) gather information or seek help to get the issue fixed, (2) change or monitor the user’s behavior in the situation, and (3) try to fix it on my own. The reasons people gave for their responses included various types of knowledge, the importance of the issue for their purpose of use, the ease with which the response could be implemented, the availability of alternative actions, the frequency and size of the error, and the range of situations within which the issue occurred. These reasons people use in selecting a response strategy differ depending on the strategy they select. Conceptual models of issue interpretation and response selection are presented to document the different relationships between cues to errors and issue interpretation, and between reasons and responses. To help with troubleshooting, designers should incorporate cues to errors to help promote the necessary understanding by the user and incorporates reasons for responses to help promote the necessary actions of the user.
Investigating proximal predictors of intraindividual affect variability in older adults

(Georgia Institute of Technology, 2016-05-04) McGlynn, Sean Andrew

The aging process is often coupled with major life changes such as retirement, death of friends and family members, and declines in physical and psychological functioning. Intuitively, any one or a conjunction of these events might be expected to lead to decreases in positive affect (PA) and increases in negative affect (NA). However, older adults tend to be emotionally positive and stable even late in life. Thus, it is possible that emotion-based strategies for coping with the challenges presented in later life can be used effectively by older adults, even amidst potential vulnerabilities in other domains. The design of effective interventions and technologies aimed at facilitating this coping process, will depend on understanding that emotions can influence health in different ways. Affect level and intraindividual variability (IIV) are independently related to distal factors such as personality and health-related outcomes such as immune functioning and mortality, among others. By nature, emotions are subject to daily fluctuations that cannot be captured by investigation of mean affect levels alone. Research on affect IIV has focused primarily on whether there are stability differences in younger and older adults. In general, older adults tend to be more stable, perhaps because the failure to regulate emotions is particularly detrimental for older adults’ physiological health. It is therefore important to understand how proximal factors in everyday life lead to intraindividual emotional changes. The primary goal of this study was to identify the factors occurring within older adults’ daily lives that predicted emotional deviations and to determine whether individuals differed in the types of factors that were emotionally-relevant. As such, it was imperative to employ a methodology that could differentiate the factors that evoked consistent emotional responses across all individuals from the factors whose impact on affect were person-dependent. Specifically, participants were given online surveys three times per day for 20 consecutive weekdays that included assessments of their current positive and negative emotional states and questions (at least once per day) about their stress, pain, sleep quality, life space, physical activity, and social activity. Multilevel modeling (MLM) was used to determine if there was significant affect IIV for these older adults and how much IIV could be explained by these proximal predictors. This analysis approach was used because it is well-suited for nested data (in this case, observations nested within-persons) and does not assume independence of observations (which is a concern when individuals receive repeated assessments). Additionally, MLM analyzes the complete dataset rather than complete cases (individuals), which allowed for comparison of fixed effects regression models and random effects regression models. Random effects models, which are the hallmark of MLM, enabled the analysis of potential individual differences in the within-person relationships between the predictors and affect. As expected, there was significant affect IIV in these older adults for both PA and NA. The predictors of PA and NA were analyzed first in isolation (referred to as “isolated models”) and then when controlling for the other proximal variables (referred to as “full models”). The random effects isolated models were generally better fitting than the fixed effects isolated models, indicating that the models that did not constrain individual predictor-affect slopes to be the same across persons (random) were more accurate representations of the observed data than models that constrained individuals’ slopes to be the same (fixed). Full fixed slopes and full random slopes models were built in stepwise fashion based on the results of the isolated models. Again, the random effects full models better fit the observed data than the fixed effects models for both PA and NA, providing strong evidence in favor of the hypothesis that a larger percentage of affect IIV would be explained when allowing individual differences in the within-person predictor-affect relationships. The full random models accounted for 32% of the PA IIV, and 45% of the NA IIV. These were both better fitting than their respective null models, indicating that overall, the proximal predictors accounted for significant proportions of the within-person PA and NA variance. Certain factors accounted for larger percentages of the IIV than others and in general, there were differences between the PA and NA model in terms of which factors led to emotional fluctuations. Subjective health accounted for the largest percentage of PA IIV and stress accounted for the largest percentage of NA IIV. Additionally, subjective health, life space, stress, and pain were significant unique predictors of PA, NA, or both. However, there were specific unique effects across both PA and NA, namely, the slope variances for stress and pain. Follow-up analyses were unable to account for these slope variances using person-level predictors. In essence, an individual’s emotional reactivity to pain and stress did not depend on his or her overall mean level of those factors, or of the other daily predictors. This provided further evidence that PA and NA should be treated as separable variables (e.g., it is possible for a daily event to decrease older adult’s positivity without necessarily increasing their negativity) but also highlighted factors that have pervasive influences on emotion regardless of valence, which is harmonious with models of affect that propose a dynamic relationship between PA and NA. The results from this study have theoretical and practical implications. Theories on emotional stability often focus on if and why older adults are more stable than younger adults. Findings of the present study both support and expand upon these theories by identifying within an older adult population, which proximal factors were likely to cause emotional deviations after partialling out the effects of other daily variables, including factors that were previously unstudied in this domain. The analysis methodology implemented in the present research allowed for direct investigation of whether certain individuals were more prone to the influences of these factors than others. These results are discussed in the context of coping and resiliency theories that posit individual differences in emotional responses to stimuli based on these capabilities. From a practical perspective, these results highlight that the design of interventions and technologies intended to provide older adults with effective skills and resources to maintain or improve their emotional well-being should be tailored to individuals’ affective profiles.
Understanding social connectedness of older adults who live alone

(Georgia Institute of Technology, 2016-04-04) Prakash, Akanksha

Ample evidence underscores the deleterious effects of loneliness on health and mortality. Therefore, it is important that loneliness risks are identified across all ages and appropriate measures are devised to address those risks. Although almost a third of the US older adult population lives alone, there is limited research on the social connectedness (or its lack thereof) in this subset of older adults. This dissertation specifically focused on understanding loneliness (its extent, variance, and sources of variance) in older adults who live alone and do not use the Internet. The results indicate that the loneliness reported in this subset of older adults is greater than that found in general older adult samples. Social isolation (measured by social network variables) and emotional well-being emerged as significant predictors of loneliness in this group. Demographics, personality, and technology experience did not predict variance in loneliness beyond that predicted by social isolation and emotional well-being. To understand if Internet adoption can provide greater opportunities for connectedness, a qualitative study was also conducted. This study focused on the subjective experiences of living alone, relationships with friends, family, and groups in the context of living alone, and the role of technology in supporting connectedness needs. Loneliness was the most commonly reported challenge associated with living alone and was often described in terms of lack of companionship or someone to share one’s feelings with. The older adult Internet users perceived usefulness of Internet-based social media as a compensatory tool for communication, but valued in-person interactions more. Together these studies provided insights into the social connectedness of older adults who live alone. The findings advanced the understanding of the complexities of living alone in older age and helped identify directions to best address social connectedness needs while also supporting older adults’ desire to continue to age in the living arrangement of their choice. Finally, the gaps in research on older adults’ use of social media and its potential to support connectedness for an aging population were also addressed.
Applying a qualitative framework of acceptance of personal robots

(Georgia Institute of Technology, 2014-11-17) Smarr, Cory-Ann Cook

Personal robots can help people live safer, more efficient and comfortable lives. However, such benefits cannot be achieved if people do not use, or accept, personal robots. The use of a technology is predominantly influenced by an individual's intention to use it, which is influenced by his or her attitudes toward it (Davis, 1989). Presently, the key factors that impact the use of personal robots are not fully understood. Two studies were conducted as first step assessments of the Smarr, Fisk, and Rogers (2013) theoretically-based framework of acceptance of personal robots. In study 1, 14 participants used a personal robot (a robot lawn mower) at their homes for about six weeks. Their acceptance and factors important for acceptance identified in the framework were measured using pre-use and post-use interviews and questionnaires, and weekly diaries. The framework was conceptually validated; participants mentioned 16 of the 20 factors in the Smarr et al. (2013) framework. However, the framework did not fully account for the breadth of factors discussed by participants, thereby suggesting variables may need to be added to or removed from the framework. In study 2, 280 participants reported their initial acceptance of a personal robot (a robot mower) with different levels of reliability and communication of feedback in an online survey. Level of robot reliability did significantly affect attitudinal and intentional acceptance. Follow up analyses indicated a trend that participants who received no information on reliability had numerically higher acceptance than participants who were informed that the robot had 70% reliability or 90% reliability. Neither communication of feedback nor its interaction with reliability affected acceptance. The Smarr et al. (2013) framework explained about 60% of the variance in intentional acceptance and 57% in attitudinal acceptance of a personal robot. Eight of the 15 relationships tested were supported via path analysis. Findings largely supported the Smarr et al. (2013) framework in explaining what impacts intentional and attitudinal acceptance of a personal robot. Results from these studies can inform the Smarr et al. (2013) framework of robot acceptance and other models of acceptance, and can guide designers in developing acceptable personal robots.
Age-related differences in fraction comparison: A process level approach

(Georgia Institute of Technology, 2014-04-10) Morgan, Michael

This study is an investigation into the relationship between numeric cognition and aging. Specifically, older and younger adults engaged in an experimental protocol that allowed observation of number comparison accuracy and response time latencies associated with the SNARC effect, the distance effect, and number format. The experimental protocol featured a computerized magnitude comparison task wherein the participants were prompted to identify the larger of two numbers. Half of the trials featured whole numbers and half featured fractions. The number stimuli were consistently mapped such that half of all trials were at near distance (i.e., difference of 2) or far distance (i.e., difference of 4) and half of all trials had the larger numerosity on the left side of space and the other half with the larger numerosity on the right side of space. Older adults were significantly slower and less accurate than young adults. Both age groups were significantly slower and less accurate when comparing fractions as opposed to comparing whole numbers. The SNARC effect impaired accuracy in both age groups but did not significantly impact response times. The distance effect impacted both age cohorts in accuracy but differentially impacted older adult response times more than young adult response times. The results of this study support the model of numeric cognition as an automatic process when comparing whole numbers at a far distance and this process is not disrupted by the SNARC effect but is when comparing whole numbers at near distance. The results also indicate that fraction comparison is a controlled process even when the fraction stimuli are consistently mapped. Further investigation is necessary to understand the amount of cognitive resources necessitated by fraction processing and if training can improve fraction comparison.