RESNA Annual Conference - 2019

Determining Needs For Technology For People With Mci/Dementia At Work: A Human Factors Approach

Karan Shastri1, Sheida Marashi1, Jennifer Boger1,2, Parminder Flora3, Arlene Astell3,4, Ann-Charlotte Nedlund5, Katja Karjalainen6, Anna Mäki-Petäjä-Leinonen6, Louise Nygård7

1University of Waterloo, Canada; 2Research Institute for Aging, Canada;

  3Ontario Shores Centre for Mental Health Sciences; Canada, 4University of Toronto; Canada,

5Linkoping University, Sweden; 6University of Eastern Finland, Finland; 7Karolinska Institute, Sweden


The number of people with mild cognitive impairment and dementia (MCI/dem) is rising rapidly with one new case every three seconds (1). According to a 2012 report by World Health Organization (WHO), 9% of the 35.6 million people with dementia worldwide are below age 65 (2). While the consequences of dementia for older adults have been extensively studied, those who experience MCI/dem before retirement age have not received much attention (3).

Occupations are defined as activities that are purposeful and have a value and meaning to the people doing them (4). Work is an important activity for most adults under 65. It can be therapeutic, promote participation in society, reduce the risk of long-term incapacity, and improve quality of life (5). However, an individual’s need to engage in work and social activity varies over time, and from person to person. Interviews with employed individuals with MCI/dem have shown that some of them preferred to stay in employment with appropriate accommodations or adjustments, while others wanted to leave work and be relieved of their vocational responsibilities (6,7).

Technology can play a part in workplace accommodation but not everyone with MCI/dem is keen on using technological adjustments unless they feel a strong need (8). Furthermore, the effect of technology use for facilitating their everyday occupations is not known (9). Some studies, such as Chaplin & Davidson’s (7) have mentioned examples of self-initiated strategies adopted by people with MCI/dem such as the use of calendars and audio recorders. The possibilities of using these strategies for problem solving has, however, often been neglected in previous studies (10).

In this paper, we pilot a Cognitive Task Analysis- Decision Centered Design (CTA-DCD) model to derive design recommendations for technology development for people with MCI/dem at work. We demonstrate the feasibility of this approach using preliminary data from interviews and a focus group with people with MCI/dem.


The figure consists of a model mentioning CTA (cognitive task analysis) and DCD (decision-centered design) frameworks. The flow of the model is as follows; the first three components belong to CTA and are knowledge elicitation, data analysis, and data representation. Data analysis is carried out through thematic analysis. Data from all three components then feeds into the work flow model. Data from work flow model flows into DCD and produces design recommendations.
Figure 1. Proposed CTA-DCD Model (reproduced from (11))
This research is part of the MCI@Work project (; a trans-Atlantic collaboration that includes the goal of using knowledge from interviews and case studies to create a digital tool to support role planning for people with MCI/dem at work. The data presented in this paper are drawn from semi-structured one-on-one interviews and focus groups. We introduce a human factors approach by using our proposed CTA-DCD model (depicted in Figure 1 and explained in more detail in (11)) to systematically elicit, analyze, and represent data in order to provide key design recommendations for technology creation. We identify macrocognitive activities through the knowledge elicitation process, which are then used to define design recommendations for a tool that could support role planning for people with MCI/dem at work.

CTA studies aim to capture what people know and how they reason, including: what they pay attention to; the strategies they are using to make decisions or detect problems; what they try to accomplish; and what they know about the way a process or system works. CTA methods also help in identifying macrocognitive activities, which are defined as the cognitive processes employed during decision making by people in complex tasks or environments. Macrocognitive activities indicate a level of description of the cognitive functions performed by people in natural decision-making settings. We use this definition to understand the cognitive processes affecting people with MCI/dem as they wrestle with difficult dilemmas, particularly in complex work settings that are often demanding, performed under time pressure, and have unexpected situations. The DCD framework focuses on tough decision making. It uses CTA methods to identify key decisions, and then translates these into decision requirements to guide the design of technology, training, and processes by narrowing down and mapping the broad macrocognitive activities to cognitive support requirements (12).

Knowledge elicitation

Data collection is being conducted through semi-structured one-on-one interviews and focus groups with people living with MCI/dem. Questions have been developed to uncover participants’ self-initiated strategies and to understand the work context by eliciting their recollections of previously experienced difficult situations and challenging events. Our goal is to understand their needs and unmet needs as well as what tools, strategies, and technology work well.

Data analysis and data representation

As we are still in the process of conducting interviews and focus groups, a descriptive data analysis consisting of people’s experience of living with MCI/dem while still employed and the barriers or problems they face at work has been conducted for this paper. The objectives of the data analysis were to identify common self-initiated strategies and artifacts at work and common macrocognitive activities. As seen in Figure 1, the workflow model is a component of the CTA that is used to represent how people get work done from the point of view of the person interviewed. The workflow model depicts the rich pattern of work as it shuttles between people, the interweaving of jobs and job responsibilities that get the work done (13).


Our discussion uses data from five people living with MCI/dem who are currently or were recently employed; three participated in one-on-one interviews and two participated in a focus group. Participant details are described in Table 1 below.

Table1. Participant demographics

Participant ID#




Type of Interview



Memory/cognitive deficits

Seasonal worker




Memory/cognitive deficits

Training manager




Early-onset dementia

City planning manager




Early-onset dementia

Support missionary

Focus group



Early-onset dementia

Case worker

Focus group

Workflow model

Figure 2 depicts the workflow model populated with some of the data obtained from the interviews and focus group; it is not exhaustive and not intended to represent everything that a person or their organization does. This is a consolidated model that contains information from all participants, the individuals are shown in the central bubble with their work responsibilities. The flow (i.e., arrows in Figure 2) depicts how they get work done, and the artifacts (i.e., tools or things they use) are shown in small boxes on flow and are physical things (e.g., Computer) or conceptual (e.g. work-buddies, discussion/conversation that aids in task completion) may warrant representation as an artifact. Artifacts tell a story about work; they reveal the strategies people use at work. Disconnects or problems faced at work, are represented as black lightning bolts and represent the problems people face in completing tasks at work. Places are shown in big boxes annotated with name of place and responsibilities and represent the places people go in and out of in order to get their work done.

Self-initiated strategies and artifacts used at work

Participants reported different strategies that they used to cope with symptoms or to cope with the extra pressures of the workplace. Examples of these are: Writing notes and memos (P1, P2, P5); making full use of a diary/daybook (P2, P5); using a virtual to-do lists app (P1); using physical to-do lists/sticky notes (P5); using a daily task management app (P5); verbal recalling technique (P2); and having work-buddy to delegate work (P3, P4).

The figure consists of a central bubble (denoted by U) mentions five participants (P1 to P5) and their corresponding work responsibilities. These include P1: Parks and city maintenance, P2: Managing training programs, P3: Planning economic development, P4: Building and construction, P5: Coordinate care. There are directional arrows (flow) from the U which depict how they get work done. There are five directional arrows from central bubble U to different bigger boxes which represent physical places where people visit to complete these tasks. Lightning bolts on these flows represent disconnects. Artifacts are represented by small boxes on flow. The first flow from U mentions “Determining training needs” with a small box mentioning “computer” and a lightning bolt and flows into a box titled “Office space” with three points inside – create programs, delegate work, and structure training programs. The second flow from U has a small box mentioning “work order” and flows into a bigger box titled “Parks” with two points inside – monitoring and physical work, and driving and planning logistics of trip. The third flow from U flow has a small box mentioning “trucks” and a lightning bolt and flows into the aforementioned “Parks” bigger box. The fourth flow from U has a small box mentioning “discussion of assignment” with a lightning bolt and flows into a bubble titled “Team” with two points – report problems and review work. The fifth flow from U has a small box mentioning “work-buddies, calendar app, sticky-notes and memos, daybooks, daily task management app, and verbal recalling” and flows into a box titled “office space and home” with one point – completing daily tasks and activities.
Figure 2. Work-flow model representing data from one-on-one interviews (n=3) and focus group (n=2).
All participants mentioned difficulties with problem solving and not being able to perform daily tasks at work efficiently. For example, performing number calculations (P2); managing team/co-worker's tasks (P3); and not knowing how to start/initiate a task at work (P1, P2, P5); spending more time planning and organizing tasks (P1, P2, P3, P4, P5).

These interviews resulted in five broad macrocognitive activities, derived from the macrocognitive processes and supporting functions: 1) managing attention, 2) mental stimulation and story building, 3) problem-solving, 4) adapting, and 5) task coordination (12).


There are currently no digital tools or assistive technology specifically designed to support people with MCI/dem at work. With this study, we introduce a human factors approach that can help to identify what features in a system or technology might better assist people with MCI/dem at work with role planning and achieving key tasks at work. The design of new systems and the modification of existing systems can benefit from a macrocognitive perspective because the design of systems often does not capture the difficult decisions people make, it rather focuses on routine requirements. As a consequence, they do not scale up; namely, they do not assist with difficult decisions made by people with MCI/dem in complex situations, especially when performing under time pressure at work. As a result, a variety of methods have emerged for injecting cognition into the design process (14).

We piloted the CTA-DCD model to systematically elicit the self-initiated strategies, artifacts being used, and understand needs from a macrocognitive perspetive. As shown in the results section, we were able to use the interviews to identify five macrocognitive activities and represent the consolidated flow of work through the workflow model. This includes representing the artifacts used as strategies, understanding the problems people faced at work through disconnects, and the physical spaces people visited to achieve tasks during work. The preliminary data collected, analyzed, and represented exemplifies the value of using a CTA-DCD approach in keeping the macrocognitive activities in the forefront.

With the preliminary data presented in this paper we can start to see how the barriers in context of the work coupled with self-initiated strategies and artifacts will guide the design of technology to better support people with MCI/dem at their workplace with role-planning and task completion. The identified macrocognitive activities will be narrowed and mapped to specific cognitive support requirements through the DCD framework. These decision requirements will then guide the design of technology. With our initial observations, we can see how some artifacts like notes, memos, to-do lists, and task-management apps coupled with barriers in the context of cognitive work like initiating, planning, and organizing tasks can lead to features/functionalities in a digital tool. When interpreting the results of this research we should keep in mind that we are proposing a new model to translate people with MCI/dem’s needs and experiences into design recommendations; as such, it has not been validated through variety of contexts.

All participants mentioned problems with attention, which resulted in feeling overwhelmed and exhausted with work. P1, P3, and P5 mention playing puzzles/cognitive stimulating games at home, and P1 mentions a need for mental stimulation as the work he was doing was not (cognitively) challenging enough. This macrocognitive activity (mental stimulation) helps us characterize inidivual barriers (work not challenging enough). In the context of cognitve work for P1, there are aspects of work that are challenging, example loss of perception of time and space while driving. However, P1 feels that the certain work tasks, such as manual labor, are not  cognitively challenging.

It is also important to note some positive aspects of leaving work such as sense of relief also reported by P5 which is consistent with the literature (15, 6) and a sense of renewed purpose through telling their story of living with dementia have also been reported (15,16).

Next steps of the project involve collecting more data through further interviews with people with MCI/dem as well as employers and co-workers who have experience working with people living with MCI/dem to validate the CTA-DCD through a variety of contexts. These data are expected to result in specific design requirements for co- developing an initial prototype of the digital tool.


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We thank our study participants for sharing their time and experiences. MCI@work is funded under the JTC 2017 as part of the Joint Programming Initiative (JPI) “More Years, Better Lives” (JPI MYBL) initiative. JPI MYBL is supported by J-Age II, which is funded by Horizon2020, the EU Framework Program for Research and Innovation, under Grant Agreement #643850.