Responding to Home Maintenance Challenge Scenarios

Abstract

This study examined potential issues faced by older adults in managing their homes and their proposed solutions for overcoming hypothetical difficulties. Forty-four diverse, independently living older adults (66-85) participated in structured group interviews in which they discussed potential solutions to manage difficulties presented in four scenarios: perceptual, mobility, physical, and cognitive difficulties. The proposed solutions were classified using the Selection, Optimization, and Compensation (SOC) model. Participants indicated they would continue performing most tasks and reported a range of strategies to manage home maintenance challenges. Most participants reported that they would manage home maintenance challenges using compensation; the most frequently mentioned compensation strategy was using tools and technologies. There were also differences across the scenarios: Optimization was discussed most frequently with perceptual and cognitive difficulty scenarios. These results provide insights into supporting older adults’ potential needs for aging-in-place and provide evidence of the value of the SOC model in applied research.

Keywords

aging-in-place home maintenance SOC model

As the world grows older, so does the desire to age successfully. Although no objective standard exists for “successful aging,” most theorists agree that one component of this construct is aging-in-place, or the ability to maintain independent, daily functioning in one’s own home (Cutchin, 2003; Kunstler, 2002). Despite age-related difficulties that could challenge successful aging-in-place, many older adults wish to stay in their homes as they grow older (Barrett, 2008; Bronstein, Gellis, & Kenaley, 2011). Moreover, older adults rate the maintenance of everyday functioning (e.g., completing hygienic activities, cooking) as an integral factor to their own quality of life (Mack, Salmoni, Viverais-Dressler, Porter, & Grag, 1997). Through processes such as selection, optimization, and compensation, individuals who wish to age-in-place must counteract social and biological losses with gains that also occur with age and experience (P. B. Baltes, 1997). Accordingly, the term “aging-in-place,” implicitly, if not explicitly, connotes the ability to overcome age-related challenges by relying on age-related gains to manage and achieve successful aging-in-place.

Broadening the Scope of Aging-in-Place

With normal aging, older adults may experience declines that can negatively influence their ability to maintain the home environment (Marsiske et al., 1999). Four of the most common abilities that decline with age are perceptual, physical (e.g., strength; dexterity), mobility (e.g., balance; coordination), and cognitive abilities (Birren & Schaie, 2006). An important question in this regard is how age-related changes in these specific abilities could impact older adults’ capacity to maintain their homes. On one hand, certain difficulties may have pervasive negative effects on a wide-range of activities. On the other hand, certain difficulties may lead to specific problems in home maintenance.

Previous research examining everyday function and home maintenance has focused on two broad classes of activities: Activities of Daily Living (ADLs) and Instrumental Activities of Daily Living (IADLs; Lawton, 1990). ADLs include essential tasks necessary for maintaining independence, such as toileting, bathing, and eating. IADLs are more cognitively demanding activities also important for maintaining a home, such as housekeeping, cooking, and managing medications. Clearly, the ability to complete ADLs and IADLs is critical to aging-in-place. However, there are many other relevant tasks not included in these classifications requisite for independent home living, such as performing small repairs (e.g., replacing a light bulb) and managing the outside of the home (e.g., mowing the lawn). We address this gap by examining a wide range of home maintenance tasks. Generally speaking, highlighting the associations between the type of difficulty and task would provide valuable information for researchers designing supports for older adults.

Strategies for Aging-in-Place

Research suggests that the majority of older adults do remain in their homes (Houser, Fox-Grage, & Gibson, 2006) and spend the largest part of the day there (Horgas, Wilms, & Baltes, 1998). The mere fact that the majority of individuals continue to live in their homes suggests that individuals are finding ways to counteract various age-related losses. Accordingly, understanding the interaction between older adults’ optimization and compensation strategies, their home environment, and the difficulties they could face in performing home maintenance tasks is critical for understanding aging-in-place (Gitlin, 2003).

One method is to modify the actual home environment. Elements of the home environment can be adjusted to the individual’s specific needs, supplementing the person’s abilities with technologies or design changes that can lead to adaptive performance of home maintenance tasks (Lawton & Nahemow, 1973). Another method involves behavioral changes enacted by the person, such as no longer doing a task, or performing it in a different way. With knowledge of the problems older adults face in the upkeep of their own homes, in addition to knowledge of the strategies used to manage these obstacles, research can guide design and promote aging-in-place. Both types of these solutions can be framed and understood in the context of the SOC model.

Selection, Optimization, and Compensation Model

The SOC model (P. B. Baltes, 1997; P. B. Baltes & M. M. Baltes, 1990) is a framework for understanding how individuals meet developmental challenges. This framework is a meta-theory that can be applied to a host of developmental processes (P. B. Baltes, 1997), including aging-in-place. The model has three critical developmental processes: selection, optimization, and compensation. In this study, we adopt an action-theoretical orientation that understands the processes of selection, optimization, and compensation in the context of goal-related actions and implementation for aging-in-place (Freund & Baltes, 2000). We further acknowledge that selection, optimization, and compensation as three facets of a single orchestrated process (Freund & Baltes). However, to assist in the measurement of these processes operational definitions of each process are still required.

Selection can be divided into elective and loss-based selection. Elective selection refers to choosing the goals one wants to pursue from all possible options. Conversely, loss-based selection involves the pruning of tasks one can no longer perform. For older adults, loss-based selection is often necessitated by normative age-related changes, such as vision loss and declining sensorimotor control (Fozard & Gordon-Salant, 2006; Seidler & Stelmach, 1995). It also results from failures of optimization and compensation to appropriately manage these losses (Freund & Baltes, 2000). This leads individuals to focus on the most important goals and loosening previously held standards (P. B. Baltes, 1997).

Optimization refers to the distribution of resources in support of maintaining performance in a selected domain (Freund & Baltes, 1998). Examples of optimization include perseverance and increased practice of goal-relevant tasks. In the context of the home this might mean continuing to vacuum the house, even if it takes longer and requires frequent breaks.

Compensation refers to the use of new means or additional processes aimed at maintaining performance in the face of resource loss. Compensatory behaviors include using technology to support performance, as well as outsourcing activities. For example, an individual might hire a person to help clean the house, or the individual might procure a tool that reduces the difficulties of performing a task.

There are several reasons why the SOC model is a useful model for understanding aging-in-place. First, the constituent, well-defined processes of the SOC model provide insights into the actual types of behaviors that older adults use to age-in-place. Second, understanding what processes are most critical for a specific task can provide guidance for interventions. Third, using the SOC model serves as a guiding framework for understanding and organizing how older adults deal with certain classes of home maintenance tasks.

Assessing SOC-Related Processes

Although the SOC is an excellent model for investigating aging-in-place, it may be difficult to assess with specificity for a particular context. A common method used is questionnaires. For example, P. B. Baltes, Baltes, Freund, and Lang (1995), presented older participants with a series of paired statements and asked them to mark which one of two statements best reflected them. For each question one option was SOC-related whereas the other was not. With this technique, individuals were given an overall “SOC score” (i.e., the total SOC-related statements they endorsed). Although easy to administer and readily interpretable, questionnaires are not easily applied to situations wherein researchers are interested in specific contexts (Bourgeois, 2003; although see Ziegelmann & Lippke, 2007, for a questionnaire approach that may be more adaptable to a context like aging-in-place). In addition, with the questionnaire method, one can obtain scores for the individual processes of selection, optimization, and compensation alone. However, measuring these processes in isolation fails to assess the critical orchestration of these processes that is at the crux of the SOC model (Freund & Baltes, 1998).

Using questionnaires also limits the breadth of data. For example, a unique and effective strategy used by an individual may not be captured by a survey/questionnaire measure because the appropriate subject matter was not asked or because the answer does not fit neatly into a response category. Alternatively, structured interviews can address some of the disadvantages of using the questionnaire method. Bourgeois (2003) examined older adults’ strategies (obtained from individual structured interviews) for managing ADLs and IADLs. Successful management of ADLs was associated with high levels of compensation. In contrast, IADLs were associated with loss-based selection and optimization. Using a similar approach, Gignac, Cott, and Badley (2002) examined how individuals with osteoarthritis managed their everyday activities. Similar to Bourgeois’ findings, selection was mentioned the fewest number of times, relative to optimization and compensation.

Structured interviews yield rich data that can provide insight into older adults’ thoughts and experiences beyond what questionnaire measures typically yield (Krueger, 1994). However, even structured one-on-one interviews have the potential to lead to “dead ends” in which an individual gets fixated on one idea. Conducting a structured interview with a group can address this limitation by creating a brainstorming session in which participants can be cued by other participant’s responses. Thus, structured group interviews can be a useful technique for understanding the breadth of SOC-related behaviors that older adults could use in maintaining their home. Furthermore, results from these types of designs may provide data which can lead to the development of questionnaire measures (e.g., Ziegelmann & Lippke, 2007)

Study Overview

We used structured group interviews to identify home maintenance tasks that older adults might find difficult to perform given four age-related difficulty scenarios (e.g., physical limitation). This allowed for an examination of the home maintenance tasks that could be affected by multiple aspects of age-related decline. Furthermore, we were interested in organizing and understanding the nature of older adults’ proposed solutions for managing these hypothetically challenging tasks using the SOC model as a guiding framework.

We provided participants with hypothetical scenarios in which they or someone in their home might be experiencing difficulties; the scenarios described perceptual difficulties, physical limitations, limited mobility, and cognitive decline. The hypothetical scenario or vignette method is an established methodology (Hughes & Huby, 2002) and is consistent with previous structured group interview research from our lab (Caine, Fisk, & Rogers, 2006; Melenhorst, Rogers, & Bouwhuis, 2006) and other applied aging research (Denton et al., 2010). Through extensive piloting, it is our experience that asking respondents about specific tasks can result in one-word answers (e.g., “no”); hypothetical scenarios remedy this problem. In addition, we have observed that older adults may be reluctant to admit needing assistance. Scenarios allow participants to distance themselves from reporting that they had a specific problem. Finally, these scenarios are interesting to participants and also limit investigator bias (Denton et al.).

Method

Participants

Twenty-four female and 20 male participants from the Atlanta metropolitan area participated in the interviews. Eighteen participants were recruited from local senior centers; 26 were recruited from the Human Factors and Aging Laboratory volunteer database. Participants were recruited to be between age 65 and 85 who either lived alone or with a spouse in a residence that required some amount of home maintenance. All participants were compensated US$10 an hour. These participants were identical to those reported in Fausset, Kelly, Rogers, and Fisk (2011). However, the data analyzed herein are from a separate part of the interviews. The characteristics of the sample are presented in Table 1. The majority of the participants rated themselves as being in good health but still having some health-related issues that limited their activities.

Table 1

Description of Participants (Replicated from Fausset, Kelly, Rogers, & Fisk, 2011).

		Race	Education (BS or higher)	Age		Health^a	Activities Limited by Health^b
Sex	Marital Status	N Caucasian/N African American	N Caucasian/N African American	Range	M (SD)	M (SD)	M (SD)
Female	Single^c	6/6	4/2	71-82	75.6 (3.5)	3.83 (0.8)	2.11 (1.5)
Female	Married	7/5	3/0	74-85	79.6 (3.5)	3.25 (1.0)	3.40 (2.0)
Male	Single^c	6/4	4/0	71-83	77.6 (4.0)	3.30 (1.2)	2.86 (1.8)
Male	Married	7/3	6/0	66-78	71.0 (3.5)	3.90 (1.0)	1.33 (1.4)

Self-rated health. 1 = Poor and 5 = Excellent.

Activities limited by health. 0 = No limitation in performing an activity; 1 = Some limitation in activity. Maximum summary variable = 6.

Single includes separated, divorced, and widowed.

Materials

Telephone prescreen

Participant eligibility was assessed via a telephone prescreening interview using standardized materials developed by the Center for Research and Education on Aging and Technology Enhancement (www.create-center.org; Czaja et al., 2006). A minimum score of 8 of 10 was required on the basic cognitive functioning assessment. To pass the working memory capacity assessment, participants were required to achieve a minimum score of 6 items recalled on the first passage or 4 items recalled on the second passage.

Structured group interview script

The structured interview script was revised after three pilot sessions (n = 12, participants representative but not in present study) to ensure that the questions were clear and prompted relevant discussion among participants. The script is available on request from the authors.

Scenarios and questions

Four scenarios were described by the moderator about common age-related difficulties: perceptual difficulties, physical limitations, limited mobility, and cognitive declines (see Table 2). The same three questions were asked about each scenario: (1) What tasks do you think would be difficult to do in maintaining your home “with scenario difficulty X?” (2) What changes could you make to your home to accommodate someone “with scenario difficulty X?” and (3) Do you know of any products, services, technologies, or remodeling options that could remedy these problems? The only exception was in the limited mobility scenario in which participants were explicitly asked what tasks would become difficult if they had to use a walker, cane, wheelchair, or scooter.

Table 2

Difficulty Scenarios Given to Participants.

Scenario	Description
Perceptual difficulties	Let us say that you have a perceptual impairment, or that someone who is living with you in your home, has a perceptual impairment. Examples of perceptual impairment include low vision or a hearing impairment.
Physical limitations	Let us say that you have one or more physical limitations, or that someone who is living with you in your home has one or more physical limitations. Such limitations could include limited range of motion, limited dexterity, or reduced strength. Limited range of motion means that it is difficult to reach, bend, stoop, kneel, or crouch. Limited dexterity means that it’s difficult to pinch, grab, turn, or twist objects with your hands. Reduced strength means that it’s difficult to lift, push, or pull objects, to raise or lower yourself, or to stand for long periods of time.
Limited mobility	Let us say that you have limited mobility or diminished balance and coordination, or that someone who is living with you in your home has limited mobility or diminished balance and coordination. This means that it is difficult to walk or to move quickly, or to be steady on your feet.
Cognitive declines	Let us say that you or someone who is living with you has cognitive limitations, or problem with their memory. Cognitive limitations might affect the ability to remember things such as where you put something, or the specific steps of a procedure.

Procedure

Participants were prescreened over the telephone to ensure eligibility. There were 11 structured interview sessions; each contained two to seven individuals, stratified by sex, marital status, and race to promote open discussions.¹ The trained moderator (two males, one female) of each interview was the same sex as the discussants. Moderators were trained to remain neutral so as not influence the discussion and to allow all individuals to speak freely (Fisk, Rogers, Charness, Czaja, & Sharit, 2009).

Before each interview, participants provided written consent. Next, the moderator informed participants of the topics and purpose of the interview. Participants were instructed to speak of their own experiences and not to interrupt others when they were speaking. Participants could endorse another participant’s response to a question but were instructed to discuss only their own thoughts and experiences. The moderator described the scenarios in the following order for all groups: perceptual difficulties, physical limitations, limited mobility, and cognitive declines. Following the description of each scenario, participants were asked the three questions. The structured group interview lasted approximately one hour. At the end of the session, participants were debriefed about the purpose of the study and compensated.

Data Analysis

Although this is a qualitative study, we were able to use a quantitative approach to ensure that the themes derived from the analysis were meaningful. All interviews were recorded and transcribed for analysis; the speakers were anonymous. Rules for segmenting or identifying idea units and initial coding schemes were developed iteratively and simultaneously by the first two authors while analyzing sample transcripts. There were three distinct coding schemes (detailed later): tasks, difficulties, and solutions. Tasks were broadly defined as specific activities performed in and around the house. Difficulties included issues associated with the scenarios provided: perceptual difficulties, physical limitations, limited mobility, and cognitive declines. Solutions referred to how participants would or could manage difficulties in general or difficulties specific to a task. It cannot be assumed that participants’ solutions would be successful or adaptive.

Transcripts were segmented by hand and then coded using the computer program MAXQDA. A segment, or a unit of meaning, was defined as an uninterrupted speaker turn that contained a single idea. A single speaker’s quotation could contain several idea units and thus produce several segments. A segment was required to mention at minimum a specific difficulty and a task or a difficulty and a solution. Difficulties could be explicitly mentioned or inferred from the moderator’s question. For example, a participant’s answer, “Hearing the telephone” was considered a segment because it mentioned a task (i.e., hearing the telephone) and a difficulty that could be inferred from the moderator’s question (i.e., perceptual difficulty). The entire segmenting (i.e., unitizing) process was conceptually similar to those outlined by other qualitative research (see Lincoln & Guba, 1985; Maykut & Morehouse, 1994).

After the segmenting rules were established, the first and second authors achieved segmenting reliability (>80%) on the first transcript. The remaining transcripts were randomly split between the first two authors and segmented individually, except for the last transcript, which was also segmented by both authors. This was done to ensure that the coders remained reliable throughout the segmenting process; reliability (>80%) was achieved on the final transcript.

Segments were then coded on the dimensions of task, difficulty, and solution. The initial coding scheme for tasks was driven by knowledge of the types of home maintenance tasks necessary for aging-in-place (Fausset, et al., 2011). For each initial code, a brief inclusion and exclusion statement was written, defining membership in that category. Through an iterative process of sampling multiple transcripts, the initial coding scheme and definitions were refined to include emergent classifications (Maykut & Morehouse, 1994). The elements of the segments were constantly compared to the initial coding scheme. When a segment contained a task that did not fit into one of these classifications, a new category was added. For example, tasks such as “hearing the telephone,” and “climbing the stairs” were not classified by the initial coding scheme, so new categories were developed (i.e., perception in the home, movement within the home) to classify these segments. When new categories were added, a brief defining statement was written and adjustments were made to the initial codes if necessary, so that it could be differentiated from the new code.

The final coding scheme included 11 categories of tasks:

ADLs (e.g., bathing, getting dressed);

Cleaning (e.g., washing dishes);

Cognitive tasks (e.g., remembering doctor’s appointments);

Home repair (e.g., fixing a leaky sink);

Home upkeep (e.g., replacing light bulbs);

IADLs (e.g., managing finances, cooking, adhering to medication regimens);

Indoor remodeling (e.g., painting);

Movement within the home (e.g., climbing stairs, bending over);

Outdoor tasks (e.g., gardening);

Perception in the home (e.g., hearing the telephone, reading);

Other.

The coding scheme for difficulties was established in a strictly top-down manner. The questions asked during the interviews were designed to highlight four commonly experienced age-related difficulties (Marsiske et al., 1999). Thus, the coding scheme for difficulties included four codes: Perceptual, Physical, Mobility, and Cognitive. A category of “Other” was included to capture comments about difficulties not related to the scenarios.

The coding scheme for solutions was developed by the first two authors through a combination of top-down influences and an iterative process of analyzing transcripts (Maykut & Morehouse, 1994). The initial coding scheme was based on common solutions reported in the literature (Fausset, et al., 2011). When a segment included a solution that was not classified by the initial coding scheme, a new category with a brief description was added.

At the end of the coding process we developed a higher-level categorization scheme guided by the SOC model (P. B. Baltes, 1997; P. B. Baltes & Baltes, 1990). We organized solutions according to four distinct categories: Elective Selection with Compensation, Elective Selection with Optimization, Loss-based Selection with Compensation, and Loss-based Selection. These categories were founded on the defining processes of the SOC model but also combined as to be able to measure some of the orchestration between these processes as well (Freund & Baltes, 2000). The codes from the final coding scheme for solutions were placed into one of these categories. Refer to Table 3 for a definition of the solution codes and example quotes.

Table 3

Selection, Optimization, and Compensation Model Classifications and Codes for Responses to Home Maintenance Difficulties.

SOC Classification	Codes	Definition	Example Quotations
Elective selection with compensation	Assistance from other	Participant is still attempting to do some part of the task but uses the assistance from another person.	“Have a friend or have someone to call everyday and say did you do so and so.” “My husband will be there, but I like that he’s gonna be there in case something happens, he’d be there to help if I need him. That’s fair, you know. I’d do that.”
	Home modification	Participant mentions a more structural and permanent change to one’s home.	“If I had to use a wheelchair in my house, the halls and the doors, they are just so small, it would be difficult. I’d probably have to do some remodeling of the doors to get in the rooms and out.”
	Tools and technologies	Participant uses any product(s) that could act as prostheses, supporting or enhancing abilities.	“I had me a gripper to reach down. I don’t have to bend over, and I use it now to pick up anything.”
Elective selection with optimization	Overt action	Participant describes modifying his/her behavior that results in a change in overt action.	“You adjust for it. If a person can’t get to the higher shelves in the kitchen, you put the things that you need [on the lower shelves].”
	Perseverance	Participant still does the task, however it takes longer and more effort to complete.	“I said I do the vacuuming. I can’t stand up and bend over as much perhaps or for as long, let’s say, as I used to, so I sit down more often. I still do the vacuuming.”
	Redesign	Participant alters the current design of the residence without bringing in new elements.	“Don’t have rugs on the floor to keep from falling.”“Getting in and out of the tub. Make sure that you got a secure place to step. You step out of the tub, make sure that you have a secure place to step.”
	Reliance on familiarity	Participant describes staying in a familiar environment or being strict about routines.	“Put your glasses in the same place every day when you take them off.”“[I] can get around my house because I know it so well, just by touching various things.”
Loss-based selection with compensation	Assisted living facility	Participant no longer does the task and instead moves to a facility where a hired professional or staff member does the task.	“If you get the mental problems you’re not gonna remember to take your medications. You’re not gonna know whether you took it or not. That’s getting to be a real problem that’s why I say they will have to take you to some place where someone could help you with these different things.”
	Outsource	Participant no longer does the task and instead hires someone or has friends, neighbors, family do the task.	“If not family members, I would try to go to a service and find an organization that provided that type of assistance, maybe.”
Loss-based selection	Task not done	Participant states that the task is not done.	“Again, forget about getting on the roof ’cause if I got on and forget where I’m at, I’m gonna fall off the roof.”

The classification Elective Selection with Compensation denoted solutions in which a person chose to continue performing a task but accomplishes it through new means, such as using a tool or technology. Elective Selection with Optimization referred to instances where an individual chose to continue performing the task and managed it without bringing in any new means to assist. Loss-based Selection with Compensation referred to using compensation to complete tasks that the individual is no longer capable of performing but necessary to maintain the home (e.g., having someone else do the task). Loss-based Selection was defined as no longer doing certain tasks because of choice or because they were unable to do so.

In sum, for this coding scheme, the underlying dimension of elective versus loss-based selected refers to whether the individual has reported attempting (at least some part) of the behavior mentioned (i.e., elective selection) or they have ceased attempting the behavior altogether (i.e., loss-based selection). The difference between optimization and compensation in this coding scheme is that with compensation behaviors new means are brought into to solve the problem whereas with optimization an individual uses their current means to accomplish a given behavior.

Reliability of coding (>80%) was achieved on one transcript by the first and second authors. The remaining transcripts were randomly split and coded individually, except for a final transcript to double check reliability. Criterion for reliability (>80%) was reached on both the first and last transcripts.

Results

Objective 1—Identifying Difficult Home Maintenance Tasks

The first objective was to identify the home maintenance tasks that older adults would find difficult to perform given four difficulty scenarios. We examined their responses to understand the nature of the tasks mentioned, as well as to highlight tasks that were mentioned most frequently in response to a specific difficulty. We attempted to understand the types of tasks affected by a range of common age-related declines as well as to understand the tasks typically mentioned in response to each specific difficulty.

To support this thematic analysis, we examined how often a specific task was mentioned across groups. There were eight distinct groups of participants across the 11 interview sessions: men or women; married or single; Caucasian or African American (see Table 1). We will discuss the response patterns as a function of these demographic groups, rather than by interview session. The frequency data are not intended to be definitive or indicative of importance or reflective of actual frequency. Rather, the rationale behind this approach was that if different groups of participants mentioned the same type of task as being difficult to perform (given a particular scenario) then it is reasonable to consider this a salient task for older adults to maintain their home.

Perceptual difficulties

Regarding the first scenario of perceptual difficulties, respondents frequently mentioned difficulty performing ADLs and IADLs. For many respondents (seven of eight groups), the primary ADL-related concerns centered around walking and moving around the house. One participant reported, “I wear my glasses all the time so I can see where I am going.”

Among IADLs, some participants expressed concerns that cooking (4 out of 8 groups) could become problematic. One participant stated,

If someone is a cook, they’re gonna have problems reading the recipes. Even just the minor involvement in the kitchen, just reading the print, knowing when it says to add this or add that and being able to be sure that you are putting those sorts of ingredients into the process or reading instructions on the boxes.

Respondents also expressed concerns about perceptual activities. Regarding visual difficulties, activities such as reading, threading needles, and seeing numbers on a telephone were discussed. With hearing, activities such as hearing the door bell and listening to the television were thought to be potential issues.

Physical limitations

When provided with the physical limitation scenario, ADLs were frequently mentioned. All groups mentioned reaching and bending as problematic. Toileting (five of eight groups) and bathing (four of eight groups) were also discussed. The primary concern with toileting and bathing was being stuck in a prone position or falling down. One participant suggested the use of bathroom bars was good because “old people always have some problems in the bathroom.”

IADLs were also mentioned by participants, most notably, cleaning tasks (seven of eight groups) such as washing dishes, using the vacuum cleaner, and doing the laundry. Related to food preparation, the specific task of opening jars was mentioned by the majority of the groups (seven of eight groups).

Several respondents (four to eight groups; mostly males) mentioned that completing outdoor activities, such as mowing the lawn, cleaning the gutters, and gardening would be difficult with physical limitations. One participant stated, “I couldn’t be gardening. I think I would find that difficult.”

Limited mobility

When provided with a scenario of limited mobility, all eight groups mentioned problems with walking around the house and climbing the stairs. The primary concern was falling. One respondent said, “It’s good for an older person to live in a small place. Small place where almost anywhere you are, you can grab on to something.”

Participants mentioned a range of cleaning tasks such as, taking out the garbage, using the vacuum cleaner, scrubbing the shower, and washing dishes to be challenging with limited mobility. One respondent stated, “The long standing, doing the dishes would probably be out,” whereas another said, “I won’t be able to perform the task of cleaning the carpet.” Cooking was mentioned by a few groups (three of eight groups) to be a problem with limited mobility.

The ADL-related activities of bathing and toileting (three of eight groups each) were mentioned in some of the group interviews. One person said, “If you own crutches, it’s a little difficult to use the restroom when you have to go in. The crutches or wheelchair could cause some problems in the average home.”

Cognitive declines

With cognitive difficulties, participants mentioned problems with two IADL tasks: managing and remembering to take their medication (six of eight groups) and cooking (eight of eight groups). One respondent knew someone “who would get up in the middle of the night, and all of a sudden turn on the water, heat water for making some tea, and then forget she was going to make some tea and go back to bed.”

Respondents also made clear that prospective memory tasks, or the ability to perform actions at some point in the future (Einstein & McDaniel, 1990), might be affected by cognitive declines (five of eight groups). Speaking generally about prospective memory failures, one respondent stated, “If I don’t get up and do it then, two or three minutes later I’m gonna be forgotten about them.” Participants mentioned a variety of prospective tasks that would become difficult to perform such as remembering doctor appointments, paying bills on time, and remembering what to buy at the grocery store.

Objective 2—Examining Proposed Solutions for Managing Difficult Home Maintenance Tasks

The second objective was to examine the solutions older adults described they would use to manage difficulties in the context of home maintenance. These solutions were framed by the SOC model, which provided a theoretical foundation for understanding these behaviors. Table 3 describes the codes within each category. In the sections that follow, we provide examples of the solutions mentioned in each of the four categories. When applicable, we also note when a particular category of solutions was mentioned more often in response to a certain difficulty relative to the others.

Elective selection with compensation

This category of responses comprised three codes: Tools and Technology, Assistance from Others, and Home Modification.

Across the four difficulty scenarios, the most frequently mentioned solution was using tools and technology. For instance, using hearing aids and magnifying glasses were mentioned to reduce perceptual difficulties; grabbers and jar openers were mentioned to ease physical limitations; chair lifts and hand rails for the bathroom were mentioned as tools to help with limited mobility difficulties; and pill boxes and cell phones to help deal with cognitive difficulties such as properly adhering to medication regimens and remembering appointments.

Assistance from others was mentioned infrequently relative to tools and technologies. When mentioned, participants talked about getting help from a spouse or friend to perform basic home maintenance tasks.

Home modification was mentioned only in response to physical limitations and limited mobility but never to perceptual or cognitive difficulties. One of the most common responses was installing ramps in the house so that a person in a wheelchair could move between rooms instead of having to use steps. In addition, respondents mentioned lowering countertops so that things could be more easily reached from a seated position and installing walk-in showers and higher toilet seats to alleviate problems with bending and movement.

Elective selection with optimization

The four codes included in this category were Overt Action, Reliance on Familiarity, Perseverance, and Redesign.

Respondents mentioned changing their behavior (i.e., overt action) to continue performing the same task when faced with a difficulty scenario. For example, one male respondent suggested, “If a person can’t get to the higher shelves in the kitchen, you put the things that you need on the lower shelf.” Even with simple tasks such as walking around the house, respondents mentioned taking their time standing up before walking. Another participant stated, “I don’t use my front door anymore,” because the individual was scared of steps.

Reliance on familiarity was mentioned when participants were prompted with perceptual and cognitive difficulty scenarios but not with physical limitations or limited mobility. With cognitive difficulties, the responses revolved around developing habits and routines that would lessen the amount of cognitive effort required for remembering. One responded said, “I’m forgetting every once and a while now. Like I said, it’s something that I got to do, then I put it where my bill folder is...I don’t even trust putting it on the . . . calendar anymore ‘cause sometimes I forget to look at the calendar.” Other participants mentioned keeping keys and eyeglasses in the same place to prevent the hassle of having to constantly look for them.

Mentions of perseverance centered on continuing to perform home maintenance tasks, even though it might take longer. In one example, a participant avowed, “I don’t have all the strength and grip to open jars now. Takes me a bit longer, and I have to keep playing with it to open the jar.”

Concerning redesign, participants often mentioned taking up area rugs as a way to ensure easier walking and reducing the chance for falls. One respondent said, “Walking on carpeting I think is harder for people that have these walkers or are very slow to move their feet. The rug sort of catches them and makes them stumble.” Another participant stated, “Make sure there are no electrical wires anywhere that you might stumble over, or catch your heel in or something.”

Loss-based selection with compensation

This category of responses included: Outsource and Assisted Living. Overall, the number of solutions coded in this category was few. For outsourcing, respondents referred to hiring a person to complete various jobs they would no longer be able to complete. One of the more striking findings was related to moving to an assisted living facility, which appeared most frequently in response to cognitive difficulties. When faced with the prospect of noteworthy declines in memory and other cognitive functions, respondents (three of eight groups) mentioned being placed is a nursing home or assisted living facility.

Loss-based selection

The only code in this category was Task Not Done. Overall, there were very few coded segments in this classification.

Discussion

There were two objectives of this study: (1) To describe the home maintenance tasks that older adults thought might be difficult to perform given various scenarios of common age-related impairments. (2) To organize the solutions older adults described they could use to manage difficult home maintenance tasks. We examined older adults’ solutions to home maintenance challenges using the SOC model as a guide. In addition to identifying themes and providing illustrative quotes, we quantified the data to identify patterns of responses. Thus, the findings and themes that emerged from this study should provide direction for follow up research.

Several types of tasks were mentioned in regard to all difficulty scenarios and there were also difficulty-specific tasks. ADLs and IADLs were mentioned as likely becoming difficult tasks across all scenarios, although there was some specificity within those classifications. Medication management (an IADL) was mentioned primarily in response to cognitive difficulties, whereas cleaning (also an IADL) was mentioned in response to physical and limited mobility difficulties. The exception to this pattern of specificity was cooking (an IADL), which was mentioned across all scenarios.

The group interviews also yielded information about home maintenance tasks beyond ADLs and IADLs. For instance, basic processes such as seeing, hearing, and walking were mentioned. These fundamental abilities are often overlooked or perhaps assumed when everyday activities are measured exclusively using ADLs and IADLs. Participants also discussed home maintenance tasks such as outdoor maintenance and generalized prospective memory tasks (e.g., remembering to get particular items at the store) that are not included in ADLs and IADLs. Thus, there are home maintenance tasks outside the realm of ADLs and IADLs that need to be considered when investigating aging-in-place.

With regard to home maintenance solutions, older adults discussed a wide range of behaviors they could use to manage perceptual, physical, mobility, and cognitive challenges. The SOC model provided categories by which to organize their strategies. These categories represent the orchestration of meta-level developmental processes, which are relevant in many areas (B. B. Baltes & Heydens-Gahir, 2003; Young, Baltes, & Pratt, 2007) including home maintenance and aging-in-place.

The majority of strategies proposed by participants involved elective selection as opposed to loss-based selection. The low rate of loss-based selection is consistent with previous work (Gignac et al., 2002). However, it is inconsistent with Bourgeois (2003) who reported loss-based selection as a common response to managing IADLs when faced with age-related losses. Given that the present participants are aging-in-place successfully, it is not surprising that there was little evidence of loss-based selection. Perhaps, if we had also interviewed individuals living in a residence with everyday activity support, loss-based selection may have been mentioned more often. Nevertheless, these findings suggest that older adults can or are willing or are prepared to manage many home maintenance tasks.

The results also suggest that older adults would use compensatory means to continue performing the majority of their home maintenance tasks. This pattern of results is consistent with Bourgeois (2003) who found that compensation was the primary method for managing ADLs when faced with age-related losses. The most common compensation strategy proposed was the use of tools and technologies. This strategy was mentioned equally across difficulty scenarios. Generally, the tools tended to be “low-tech,” inexpensive, relatively easy to use, and required little to no maintenance on the part of the user (e.g., post-it notes; calendars). Of course, there were mentions of “high-tech” tools such as cell phones and pill boxes with alarms. The wide variety of tools and technologies mentioned by the participants as potential solutions for managing various challenges suggests that older adults are aware of tools that can help them perform home maintenance tasks.

There was also evidence of optimization as a strategy for completing home maintenance tasks. Reliance on familiarity appears to be a unique form of optimization. By definition, optimization includes the practice of skills or increased effort directed toward maintaining performance. Reliance on familiarity in a sense is the opposite because individuals do not try harder at all. However, it should still be considered optimization because individuals practice the same routines, lowering the amount of resources needed to perform a given task, and perhaps eventually, using environmental context rather than attention to trigger performance of that given task (Caine, Nichols, Fisk, Rogers, & Meyer, 2011; Fisk & Rogers, 1991). Thus, this behavior is distinct from practice in the sense that these behaviors do not need to be learned because they are already automatized. Rather, this behavior is more akin to something like habit formation. It is related to the optimization-related behavior of “seizing the moment” (Freund & Baltes, 2000) in the sense that in order to develop a habit the behavior needs to be activated at the right times and consistently. To our knowledge, reliance on familiarity has not been included in previous operational definitions of optimization. One reason may be that previous studies have focused on physical issues and have not fully explored the role that cognitive abilities play in successful home maintenance (Gignac et al., 2002; Marsiske et al., 1999). The importance of reliance on familiarity for aging-in-place is an interesting area for further exploration and should be incorporated into future conceptualizations of optimization.

Given that tools and technologies were mentioned as solutions to manage cognitive declines as well, it suggests that older adults can develop multiple ways to solve a particular problem. Future research should be directed at understanding the combined influence of habit development along with the use of tools and technologies.

Loss-based selection with compensation was mentioned several times, although not as frequently as other types of solutions. This response arose most notably in reference to cognitive declines. Specifically, multiple older adults mentioned that if their cognitive faculties declined significantly, they would have to move to an assisted living facility. This is consistent with Ball, Perkins, Hollingsworth, Whittington, & King (2009) who reported that the presence of Alzheimer’s disease was a significant “push factor” for at least one individual in the decision to relocate to an assisted living facility. This suggests that aging-in-place may be most threatened by the severe, almost dementia-like symptoms of cognitive decline.

From a methodological perspective, the current project included several unique features in the study of aging-in-place. First, we placed the focus on how hypothetical difficulties could impact older adults’ in their ability to perform home maintenance tasks instead of focusing on specific tasks. This allowed us to ask individuals to think more broadly about the tasks they might need to perform to maintain their homes. Even within this context, the tasks mentioned by participants were fundamental actions in nature (e.g., climbing, bending) instead of task-specific.

Second, we used structured group interviews, instead of questionnaires, allowing us to gather information on a variety of home maintenance tasks and solutions that older adults (would) use to manage their homes. A major benefit of using group interviews is that participants can build off each other’s thoughts and ideas (Krueger, 1994), providing a more detailed understanding of the aging-in-place problem space. Future research can use this information to develop questionnaires and specific measures to investigate particular elements of aging-in-place.

Third, we combined categories from the SOC model (e.g., Elective Selection and Compensation) instead of mutually exclusive categories to code the solutions. This approach allowed us to capture the orchestration of processes in the SOC model. Although this approach has several benefits, such as making explicit the ways in which the processes of selection, optimization, and compensation work together, our classification may not have been completely faithful to the ideals of the theory. For instance, compensation in the face of loss-based selection diverges from the literature that suggests compensation cannot happen with loss-based selection (e.g., Freund & Baltes, 2000). However, this strategy is likely adaptive as it indicates the person is managing his/her loss beyond just stopping goal pursuit. In loss-based selection with compensation, people are managing their loss through compensatory means. However, loss-based selection indicates people are not managing their loss effectively as they are no longer doing the task and there is no indication that the task is being done at all. Loss-based selection is likely maladaptive in the context of home maintenance and aging-in-place.

In addition, the resources and goals of a particular individual in a given situation must first be understood to understand their strategies (e.g. Ziegelmann, & Lippke, 2007). We attempted to get at this information through coding of elective- and loss-based selection. In other words, differentiating between these two processes in our coding scheme provides some insights into individual’s resource level, albeit imperfectly. In addition, our analysis was operating under an implicit assumption that all individuals in regards to aging-in-place have the same goal, namely that of continuing the behaviors that allow them to age-in-place. Admittedly, this assumption may not apply for all behaviors and all individuals. As such future research that attempts to understand the interplay between SOC-related processes would be well served to carefully measure individuals’ resources and goals.

Despite these limitations, our results demonstrate the usefulness of applying the SOC model to specific areas of interest. We were able to organize older individuals’ strategies and responses for dealing with home maintenance difficulties into a coding scheme that reflected the interplay of basic developmental processes, which is not always evident when these processes are measured. Using the SOC model will lead to a more complete understanding of the mechanisms underlying aging-in-place, and can guide the development of new technologies and services to support individuals in pursuit of independence in late life. In addition, future studies should also work to refine the categories presented here so that they are more fully in accordance with the SOC theory. Finally, it would be beneficial for future research to use the SOC model to investigate other components of aging-in-place, such as medication adherence, nutrition planning, and so on.

There were limitations of this study that must be acknowledged. First, we provided participants with scenarios about possible age-related difficulties and asked them to describe the home maintenance tasks that could become difficult to perform as well as solutions to these issues. Therefore, we cannot determine the tasks that older adults actually have problems doing or the solutions they actually use to manage those difficulties.

In addition, because this was a qualitative study, we were not able to gather reliable information about the frequency with which each solution was described. For our purposes this was acceptable given that we were interested in cataloging the solutions described. However, future research could use the solutions reported here to develop a measure designed specifically for obtaining accurate frequency information for a large sample.

Conclusion

In conclusion, the present results demonstrated that older adults would be most likely to manage common age-related difficulties by using tools and technologies. This further promotes the need for designing and manufacturing easy-to-use tools that alleviate difficulties faced around the home. Our results also illustrate the importance of reliance on familiarity as a strategy for dealing with cognitive difficulties around the home. Future research should explore this type of optimization solution, as it appears to be a useful strategy for older adults and not explicitly defined in the SOC literature. Finally, this study has further established the use of the SOC model as a useful framework for understanding aging-in-place. Our results demonstrate the importance of distinguishing between elective- and loss-based selections. Future studies should make this distinction so that tasks associated with loss-based selection can be the focus of new interventions.

Footnotes

Author’s Note

We would like to thank Andrew Mayer for his assistance with data collection. Portions of these data were presented at the 13th Cognitive Aging Conference in Atlanta, Georgia.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This research was supported in part by a grant from the National Institutes of Health (National Institute on Aging) Grant P01 AG17211 under the auspices of the Center for Research and Education on Aging and Technology Enhancement (CREATE; ).

Notes

Author Biographies

Andrew John Kelly received his PhD in cognitive aging from the Georgia Institute of Technology and is now Assistant Professor of Psychology at Georgia Gwinnett College. His primary research interests are in the area of aging and memory.

Cara Bailey Fausset received her PhD in engineering psychology from the Georgia Institute of Technology as a member of the Human Factors and Aging Laboratory. Her primary research interests are in cognitive aging, health numeracy, and aging in place.

Wendy A. Rogers is Professor in the Engineering Psychology and Cognitive Aging programs at the Georgia Institute of Technology. She received her PhD in psychology from the Georgia Institute of Technology in 1991.

Arthur D. Fisk is Professor in the School of Psychology at Georgia Tech. He is a past president of the Human Factors and Ergonomics Society and a past editor of Human Factors.

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