HUMAN
FACTORS,
1990,32(5),537-549
Older Adults and Daily Living Task Profiles M. CHERIE CLARK,l SARA J. CZAJA, and RUTH A. WEBER, Stein Gerontological Institute, Miami Jewish Home and Hospital for the Aged, Miami, Florida
This paper describes data generated from a comprehensive study in which human factors techniques were applied to the analysis of 25 personal and instrumental activities of daily living (ADLs) performed by 60 older adults (age 55 to 93 years) living in the community. Demand profiles generated from task analysis of videotaped ADLs identify the demands inherent in task activities and the objects and environments associated with them. Examples of four different approaches to examine ADL performance using demand profiles are presented: global descriptions of demands across all ADL tasks, task component profiles, task-specific profiles, and action profiles.
INTRODUCTION It is generally recognized that changes in physical and psychological capabilities which accompany the aging process affect the ability of older adults to function effectively in community settings. Research has demonstrated that daily living activities such as bathing and meal preparation are often problematic for older adults (e.g., Faletti, 1984; Fozard, 1981; National Center for Health Statistics, 1987a). For example, a recent study (Czaja, Clark, Weber, and Faletti, 1988) found that older people frequently encounter difficulties performing tasks such as grocery shopping, meal preparation and cleanup, and bathing/showering. Similarly, Czaja and Drury (1987) found that older people incur a high rate of accidents while performing rouI Requests for reprints should be sent to M. Cherie Clark, Stein Gerontological Institute, Miami Jewish Home and Hospital for the Aged, 5200 NE 2nd Ave., Miami, FL 33137.
tine home tasks. Clearly the ability to perform daily living activities is a major factor influencing the older person's continued independence in the community versus placement in an institutional setting. It has been suggested (Faletti, 1984; Lawton, 1977) that the inability of older people to function effectively in residential settings is a person-environment problem. This is consistent with a transactional model of human performance, which suggests that successful performance of a task is dependent on a match between the individual's capabilities and demands generated by the task. Task demands are generated by the performance requirements of a task (e.g., lift) and the designed environment (e.g., counter height). According to this view, the older person's inability to successfully complete daily living activities is the result of a mismatch between the demands inherent in these activities and the capabilities of the older person.
© 1990, The Human Factors Society, Inc. All rights reserved.
HUMAN
538-0ctober 1990
Essentially the transactional model of performance views the problem of maintaining independent functioning as one of matching the designed environment with user populations. This model, which is consistent with a human engineering model of performance, suggests that the task performance of older adults might be improved by changing the design of products and environments to accommodate age-related changes in functional abilities. However, this assertion is predicated on an understanding
of the demands
associated with environments and daily tasks and of the capabilities of older adults relative to these demands. Essentially two types of data are warranted: data specifying demands associated with daily living activities and data specifying physiological and psychological capabilities of older adult populations. Currently there are gaps in empirical knowledge with respect to person-environment transactions for older adults (Faletti, 1984). For example, available anthropometric and biomechanical data describing older adult populations are limited and unsystematic (Stoudt, 1981). In addition, there has been limited work relating specific age-related changes in function to corresponding changes in ability to perform daily living tasks. To date most research and design efforts have focused on either methods for assessing older adult abilities or designing housing and support services to provide assistance for older persons in community settings. Data regarding the ability of older adults to perform living tasks is based largely on selfreport and is primarily restricted to frequency of problems older people have with these tasks. There are limited data on the types of problems encountered. Currently the best data are from the National Health Interview Survey (National Center for Health Statistics, 1987b) in which respondents are asked if they experience difficulty performing a task
FACTORS
and if they receive help with the task. Findings from this survey indicate that tasks that are most problematic for older adults are walking, bathing, transferring, preparing meals, shopping, and doing heavy housework. Although this type of data is useful in targeting areas of needed research, its usefulness is limited with respect to identifying specific intervention strategies or making decisions about the ability of older adults to remain at home. In addition to self-report data, we need data that operationalize the relationship between functional processes and performance demands inherent in ADL tasks. The present paper describes data generated from a comprehensive study in which human factors techniques were applied to the analysis of both personal activities of daily living (ADLs) and instrumental daily living tasks (IADLs) performed by older adults living in the community. The objectives of the study were to identify and describe the physiological demands associated with these activities. In this paper data from the analyses of selected tasks are presented in order to demonstrate how demand profiles for ADL and IADL tasks can be generated. These profiles define demands inherent in task activities and the objects and environments associated with them. Demands are further specified through objective measures of relevant environments and objects. The data generated from this study can subsequently be compared with anthropometric and biomechanical characteristics of older people to identify components of living tasks that may be problematic for older adults. In addition, they can be used to specify appropriate prevention and intervention strategies, including environmental product design, assistive technologies, and support services. Exar.tples are provided to illustrate the utility of human factors methodologies in analyzing daily living activities.
October 1990-539
ADL TASK PROFILES
MATERIALS AND METHODS Task demand profiles were generated from videotape data of older adults performing ADL and IADL activities and objective measurements of task objects and environments. These data bases are described in more detail later in this paper. Data were collected for 25 personal and instrumental tasks including cooking meals, doing laundry, and shopping for groceries; housecleaning tasks such as scrubbing floors, vacuuming, and changing beds; operating appliances, locks, and light switches; and performing personal care tasks such as bathing, dressing, toileting, taking medicine, and getting in and out of a bed or a chair. Initially the tasks were identified by means of an ADL performance questionnaire. This questionnaire was administered to a sample of 244 independently living older adults between the ages of 55 and 93 years and assessed frequency of ADL performance, amount of help received, and problems en-
countered in completing ADLs (Czaja et aI., 1988). Although these tasks may not include all tasks that an older person must accomplish, they are critical to daily living and representative in terms of demands. In addition, the questionnaire data revealed that they are tasks that most older persons do regularly and with which they may experience problems (see Table 1). This is consistent with national data (National Center for Health Statistics, 1987b) regarding tasks that are problematic for older people. Our data also show that meal preparation, shopping, bathing, and housework are difficult to perform. Sample Sixty persons (11 single males, 30 single females, and 19 members of couples) who constituted a subset of the questionnaire respondents were included in this phase of the research. The age range was 55 to 93 years with a mean of 72 years. The majority of participants (87%) resided in single-family homes or apartments/condominiums. Indi-
TABLE 1
Percentage ing Tasks
of Individuals
Who Reported
at Least One Problem with Daily livGroup
Task Category
Household tasks Meal preparation Grocery shopping House cleaning Laundry Personal tasks Dressing Bathing Grooming Transfer tasksB Management tasksb
Males
n = 57
Females n = 184
Total 244
n =
x2
5
47 55 39 14
45 53 31 12
0.5 1.3 21.2"* 2.2
23 16 16 10 10
35 33 32 34 14
32 28 28 28 13
2.6 5.2* 4.9* 10.8*** 0.2
40 46 5
The chi-square statistics have been adjusted using Yeal's correction. • Getting in and out of bed, chairs, bathtub, etc., or using stairs. bUsing telephone, accessing mail, operating locks . •p < 0.05 .•• p < 0.01 .••• p < 0.001.
540-0ctober
1990
viduals were compensated $25 for participating in the study. Method Two research assistants used portable videocassette recorders and color cameras to record each participant's movements. Persons were videotaped in their homes, at the laundromat, and in the grocery store; taping took two days in most cases. Task analysis was accomplished using a program that links a videotape player to a personal computer using a BCD videolink. The task analysis software written for this project controls the videotape player from the computer keyboard. It is menu driven, and the selection of tasks, actions, demands, objects, and locations is standardized. In addition, the program records frame numbers on the videotape itself so that specific sequences of tasks can be accurately and easily accessed for more detailed coding or reliability checks (Weber, Czaja, and Redmond, 1988). The task analysis coding system is a hierarchical decomposition by which tasks are first divided into component activities and then these activities are specified in terms of environmental demands. The environmental demands are translated into person actions. The location of the object to be acted on during task performance determines the posture assumed while performing the task; the object itself defines the grip assumed by the hand during the operation; and the function of the environmental demand (e.g., open, close, reposition) determines the necessary action performed to accomplish the demand (e.g., push, pull, lift, or lower). For example, the person actions associated with the task of removing a can of vegetables from a low shelf in the grocery store would be a bending posture to access the can, a palm grip around the can, and a lift to reposition the can (remove
HUMAN
FACTORS
from the shelf). Actions, postures, grips, ob· jects, and locations were coded for each task activity. Across all 25 ADL tasks 21 586 actions were coded. Table 2 is a breakdown of the number of actions per task and the number of subjects whose actions were coded. For purposes of analysis, observations were weighted according to the number of videotapes (subjects) coded for each task. Environmental measures were taken of the homes of a subsample of 47 of the individuals who had been videotaped performing the ADLs. Measurements included room dimensions and height of light switches, doorknobs, countertops, appliance tops, shelves, and so on. An inventory was also taken of the pieces of furniture and the types of appliances, locks, lamp switches, cabinet handles, and so on, in each room. The force to open doors, turn on faucets, and activate appliances was measured, as was the distance to mail, laundry, and trash facilities. Size of appliances and furniture was calculated, as was the amount offree floor space. Measurements were taken in all rooms of the dwelling (kitchen, bathroom, dining room, living room, and bedrooms). In addition, all the objects handled during task performance (door handles, appliance knobs, food containers, laundry baskets, cooking utensils, vacuum cleaners, etc.) were weighed and measured. To date more than 500 objects have been measured. RESULTS AND DISCUSSION The raw data from the task analysis of the videotapes was used to generate task demand profiles for each of the 25 tasks. A demand profile (see Figure 6 for an example) provides a detailed description of task activities and the physiological performance demands inherent in these activities. Once generated, these profiles can be analyzed in a number of
October 1990--541
ADL TASK PROFILES
TABLE 2 Demand-Action Observations by Task and Subjects ADUTask Bathing Bed changing Bed ingress/egress Brushing hair Chair ingress/egress Cleaning appliances Cleaning bathroom Communicating Dressing/undressing Dusting Floor scrubbing Floor sweeping Taking out garbage Grocery shopping Doing laundry Turning lights on/off Operating locks Mailing a letter Preparing a meal Medicine taking Brushing teeth Toileting Activating TV/radio Vacuuming Washing dishes
Observations
Subjects
601 541 108 371 107 664 670 217 362 491 1132 606 116 8339 3566
14 5
5 13
13 6 5 19 5 6 5 5 5
22 16
94
18
87
9 11 4
101 612 242 484
80 149 1142 704
ways depending on the goal of the analysis. For example, the data may be analyzed across all tasks or wi thin a task focusing on specific task demands. In this paper examples of four different approaches to using demand profiles are presented: global task descriptions, task component profiles, task-specific profiles, and action profiles. The intent is to demonstrate that by using human factors techniques to analyze daily task performance, a very rich data base is produced that can be examined in a variety of ways. The appropriate analytic approach depends on the desired outcome. For example, using these data to generate housing or product designs would require an analysis different from one using the data to predict the functional performance of frail elderly.
11
6 22 18
5 4
Observations per Subject (SD) 42.9 (26.3) 108.2 (137.3) 21.6 (14.1) 28.5 (12.3) 8.2 (4.9) 110.7 (127.3) 134.0 (96.2) 11.4 (7.4) 72.4 (92.2) 81.8 (49.1) 226.4 (199.0) 121.2 (120.4) 23.2 (13.6) 379.0 (350.3) 222.9 (155.9) 5.2 (8.1) 9.7(5.1) 9.2 (3.4) 153.0 (52.7) 22.0 (19.3) 80.7 (47.0) 3.6 (3.8)
8.3 (6.4) 228.4 (121.7) 176.0 (163.3)
Global Task Descriptions Global task descriptions specify the performance demands that are inherent in all ADL tasks. Figures 1-5 present frequency histograms for these global demands. As shown in Figure I, lift/lower and push/pull movements are the most frequent motions performed across all tasks (together they account for 60% of all actions). With regard to grips and postures (Figures 2 and 3), precision grips are used most often (40% of all hand usage), and the standard work posture (48%), lean reach (21%), and bend (14%) accounted for 83% of all postures. Of the tasks, 95% involve the use of the upper body-that is, hands and/or arms (Figure 4). Chi-square analyses suggest that it is rea-
542-0ctober 1990
HUMAN
FACTORS
50% 1. Lift/Lower 2. Push/Pull 40% 36.8%
3. Hold, Carry, Suspend 4. Rotate 5. Side to Side
30%
6. Hand to Hand 7. Fold 8. Look
20%
9. Drop or Throw 10. Reach
10%
11.Other
0% 2
3
4
5
6
8
7
9
10
11
Figure 1. Actions across all tasks.
sonable to sum across ADL tasks to secure information on actions (motions), body part (specifically grip), and posture because actions, grips, and postures are independent of ADL task (ps < 0.05). However, the location where the action takes place and the objects acted on are task dependent (ps > 0.05). For
example, during meal preparation the most frequently handled objects are food containers (for instance, jars, cans, boxes, and cooking utensils, constituting 49%), whereas while doing laundry the most frequently handled objects are clothing and bed linens (63%). A summary of the most frequently
50% 1. No Grip
42.1%
2. Precision
40%
3. Power 4. Palm 5. Cradle
30%
20%
10% 2.39%
0% 2
3
4
Figure 2. Hand grips across all tasks.
5
October 1990---543
ADL TASK PROFILES
1. Standard
Work
2. Lean Reach 3. Bend 4. Sit 5. High Reach 6. Stoop 7. Extra
High Reach
8. Other
0.50/.
5
6
8
7
Figure 3. Postures across all tasks. handled objects across all tasks is presented in Figure 5. The data from this analysis suggest that there are task components that are common to most ADL and IADL activities. This implies that ADL tasks do not have to be considered as independent activities, which is an
important finding with respect to the issue of assessment. Because there are performance demands common to ADL and IADL tasks, an assessment protocol that incorporates these common demands might be able to successfully predict level of ADL and !ADL impairment. For example, the data suggest that if a
50% 43.5%
1. Right
Hand
2. Both Hands 3. Left
Hand
4. Arm(s) 5. Leg(s) 6. Other
4.5%
2.1% 4 2 3 Figure 4. Body parts across all tasks.
2.1% 5
6
HUMAN
544-0ctober 1990
FACTORS
50%
1. Grocery Item 2. Laundry 40%
41%
3. Cleaning Cloth, Sponge, etc. 4. Cleaning Supply 5. Door
30%
6. Bedding 7. Money 8. Mop or Broom
20%
9. Purse or Wallet 13.4% 13.3%
10. Storage
Container
5
7
10%
0% 2
3
4
6
8
9
10
11
Figure 5. Objects across all tasks.
person is able to lift and lower, push and pull, assume a precision grip, stand for extended periods, and reach from a relatively small angle, then that person should be able to complete most routine tasks. By supplementing the task analysis data with environmental and object measures (e.g., object weight, shelf height), a more specific assessment tool for ADL task performance can be constructed. For example, a person must be able to reach to a certain height, lift objects of a certain weight, and pull with a certain force to meet the demands of the specific objects/environments used in the various tasks (e.g., removing laundry from a washer). How well global task demands are met will determine the degree of independence and identify the need for assistance. Task Components Profiles
A similar analytic approach involves summing data across all ADL tasks for groups of similar task components-for example,
grouping types of handling characteristics across tasks. All motions can be grouped as either transport tasks (moving an object from one place to another), complex manipulation tasks (motions involving movement of the fingers, hands, wrist, and arm in several planes), or gross manipulation tasks (those involving simple and sometimes repetitive movement within a single plane of motion). Our data, across all ADL tasks, show that 37% of the demands required the transport of an object without acting on it-for example, retrieving a grocery item from a shelf or moving laundry from the washer to the dryer; 16% of the demands required some sort of fine hand manipulation such as chopping a vegetable, inserting a key, or turning an appliance off or on; and 51% were of an intermediate level requiring more gross manipulation, such as pushing a vacuum cleaner, wiping a counter, or folding laundry. This analysis approach can be used to identify and prioritize needed areas of intervention and suggest areas where human factors
October 1990-545
ADL TASK PROFILES
engineering can be applied to reduce task demands. For example, the data suggest that assistance with transport movements might have a major impact on ADL performance. Examination of objective dimensions of transport tasks will suggest ways in which the design of products or environments may be altered to better meet the capabilities of older people. For example, storage locations may be better placed to reduce long-distance transport of heavy objects such as vacuum cleaners. Lighter-weight products could be developed for objects transported with high frequency, and handle dimensions could be changed to allow better control through the use of more effective grip types. Assistive technologies, including movable storage units or a track-mounted robotic arm, could be designed and installed to complete or assist frail elderly with these transport movements. Given the increased incidence of osteoarthritis of the hand in the older population (Plato and Norris, 1979), attention to specific task parameters associated with transporting objects could greatly improve design of assistive technology and enhance daily living for a great many older persons. Grouped task component data can also be used to generically assess the effect of specific disabilities on daily task performance and target interventions for afflicted individuals. For example, persons with osteoarthritis or bursitis in the shoulder have difficulty performing a large number of tasks because transporting objects is a component of most tasks and is difficult to perform with limited shoulder mobility. Persons with these disabilities would benefit most by technologies designed to assist with transport tasks. A person with limited finger and hand flexibility would require more complex assistive technology or intensive support services because both manipulation tasks and transport tasks would be difficult, given the handling de-
mands of these tasks. Information of this type would be useful for care givers who provide support services to older persons. Task-Specific Profiles An additional analytic approach is to examine individual ADL tasks. For this study, task demand profiles were generated for each of the 25 specific ADL tasks. A task-specific approach is desirable when the interest is in designing or assisting task performance within a specific environment or for a specific task. For example, a grocer's association would be interested in task profiles only for grocery shopping, not for housecleaning or doing laundry, whereas a human factors engineer interested in generating guidelines for eldery housing would be interested in data associated with tasks such as meal preparation or bathing/showering. Figure 6 presents a component map for one ADL-changing bed linens-to illustrate the efficacy of this approach. Most older persons perform this task on a weekly basis and report it as problematic. The most frequently reported problem with bed changing is the bending and leaning movements it requires. This becomes an obvious prediction when examining the component data. To change bed linens, one must operate at a work height (bed height) of 55.88 em, which requires bending postures 49% of the time and leanreach postures 26% of the time. Lift/lower (29%) and push/pull (23%) are the most frequent actions performed. It is clear that a simple redesign involving raising the height of the bed could greatly reduce the problematic demands associated with changing bed linens. However, raising the height of the bed may make it difficult for older people to get in and out of bed and create a potential for fall accidents. A bed with easily adjustable height might be the best alternative. Such a device has been designed
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FACTORS,
1990,32(5),537-549
Older Adults and Daily Living Task Profiles M. CHERIE CLARK,l SARA J. CZAJA, and RUTH A. WEBER, Stein Gerontological Institute, Miami Jewish Home and Hospital for the Aged, Miami, Florida
This paper describes data generated from a comprehensive study in which human factors techniques were applied to the analysis of 25 personal and instrumental activities of daily living (ADLs) performed by 60 older adults (age 55 to 93 years) living in the community. Demand profiles generated from task analysis of videotaped ADLs identify the demands inherent in task activities and the objects and environments associated with them. Examples of four different approaches to examine ADL performance using demand profiles are presented: global descriptions of demands across all ADL tasks, task component profiles, task-specific profiles, and action profiles.
INTRODUCTION It is generally recognized that changes in physical and psychological capabilities which accompany the aging process affect the ability of older adults to function effectively in community settings. Research has demonstrated that daily living activities such as bathing and meal preparation are often problematic for older adults (e.g., Faletti, 1984; Fozard, 1981; National Center for Health Statistics, 1987a). For example, a recent study (Czaja, Clark, Weber, and Faletti, 1988) found that older people frequently encounter difficulties performing tasks such as grocery shopping, meal preparation and cleanup, and bathing/showering. Similarly, Czaja and Drury (1987) found that older people incur a high rate of accidents while performing rouI Requests for reprints should be sent to M. Cherie Clark, Stein Gerontological Institute, Miami Jewish Home and Hospital for the Aged, 5200 NE 2nd Ave., Miami, FL 33137.
tine home tasks. Clearly the ability to perform daily living activities is a major factor influencing the older person's continued independence in the community versus placement in an institutional setting. It has been suggested (Faletti, 1984; Lawton, 1977) that the inability of older people to function effectively in residential settings is a person-environment problem. This is consistent with a transactional model of human performance, which suggests that successful performance of a task is dependent on a match between the individual's capabilities and demands generated by the task. Task demands are generated by the performance requirements of a task (e.g., lift) and the designed environment (e.g., counter height). According to this view, the older person's inability to successfully complete daily living activities is the result of a mismatch between the demands inherent in these activities and the capabilities of the older person.
© 1990, The Human Factors Society, Inc. All rights reserved.
HUMAN
538-0ctober 1990
Essentially the transactional model of performance views the problem of maintaining independent functioning as one of matching the designed environment with user populations. This model, which is consistent with a human engineering model of performance, suggests that the task performance of older adults might be improved by changing the design of products and environments to accommodate age-related changes in functional abilities. However, this assertion is predicated on an understanding
of the demands
associated with environments and daily tasks and of the capabilities of older adults relative to these demands. Essentially two types of data are warranted: data specifying demands associated with daily living activities and data specifying physiological and psychological capabilities of older adult populations. Currently there are gaps in empirical knowledge with respect to person-environment transactions for older adults (Faletti, 1984). For example, available anthropometric and biomechanical data describing older adult populations are limited and unsystematic (Stoudt, 1981). In addition, there has been limited work relating specific age-related changes in function to corresponding changes in ability to perform daily living tasks. To date most research and design efforts have focused on either methods for assessing older adult abilities or designing housing and support services to provide assistance for older persons in community settings. Data regarding the ability of older adults to perform living tasks is based largely on selfreport and is primarily restricted to frequency of problems older people have with these tasks. There are limited data on the types of problems encountered. Currently the best data are from the National Health Interview Survey (National Center for Health Statistics, 1987b) in which respondents are asked if they experience difficulty performing a task
FACTORS
and if they receive help with the task. Findings from this survey indicate that tasks that are most problematic for older adults are walking, bathing, transferring, preparing meals, shopping, and doing heavy housework. Although this type of data is useful in targeting areas of needed research, its usefulness is limited with respect to identifying specific intervention strategies or making decisions about the ability of older adults to remain at home. In addition to self-report data, we need data that operationalize the relationship between functional processes and performance demands inherent in ADL tasks. The present paper describes data generated from a comprehensive study in which human factors techniques were applied to the analysis of both personal activities of daily living (ADLs) and instrumental daily living tasks (IADLs) performed by older adults living in the community. The objectives of the study were to identify and describe the physiological demands associated with these activities. In this paper data from the analyses of selected tasks are presented in order to demonstrate how demand profiles for ADL and IADL tasks can be generated. These profiles define demands inherent in task activities and the objects and environments associated with them. Demands are further specified through objective measures of relevant environments and objects. The data generated from this study can subsequently be compared with anthropometric and biomechanical characteristics of older people to identify components of living tasks that may be problematic for older adults. In addition, they can be used to specify appropriate prevention and intervention strategies, including environmental product design, assistive technologies, and support services. Exar.tples are provided to illustrate the utility of human factors methodologies in analyzing daily living activities.
October 1990-539
ADL TASK PROFILES
MATERIALS AND METHODS Task demand profiles were generated from videotape data of older adults performing ADL and IADL activities and objective measurements of task objects and environments. These data bases are described in more detail later in this paper. Data were collected for 25 personal and instrumental tasks including cooking meals, doing laundry, and shopping for groceries; housecleaning tasks such as scrubbing floors, vacuuming, and changing beds; operating appliances, locks, and light switches; and performing personal care tasks such as bathing, dressing, toileting, taking medicine, and getting in and out of a bed or a chair. Initially the tasks were identified by means of an ADL performance questionnaire. This questionnaire was administered to a sample of 244 independently living older adults between the ages of 55 and 93 years and assessed frequency of ADL performance, amount of help received, and problems en-
countered in completing ADLs (Czaja et aI., 1988). Although these tasks may not include all tasks that an older person must accomplish, they are critical to daily living and representative in terms of demands. In addition, the questionnaire data revealed that they are tasks that most older persons do regularly and with which they may experience problems (see Table 1). This is consistent with national data (National Center for Health Statistics, 1987b) regarding tasks that are problematic for older people. Our data also show that meal preparation, shopping, bathing, and housework are difficult to perform. Sample Sixty persons (11 single males, 30 single females, and 19 members of couples) who constituted a subset of the questionnaire respondents were included in this phase of the research. The age range was 55 to 93 years with a mean of 72 years. The majority of participants (87%) resided in single-family homes or apartments/condominiums. Indi-
TABLE 1
Percentage ing Tasks
of Individuals
Who Reported
at Least One Problem with Daily livGroup
Task Category
Household tasks Meal preparation Grocery shopping House cleaning Laundry Personal tasks Dressing Bathing Grooming Transfer tasksB Management tasksb
Males
n = 57
Females n = 184
Total 244
n =
x2
5
47 55 39 14
45 53 31 12
0.5 1.3 21.2"* 2.2
23 16 16 10 10
35 33 32 34 14
32 28 28 28 13
2.6 5.2* 4.9* 10.8*** 0.2
40 46 5
The chi-square statistics have been adjusted using Yeal's correction. • Getting in and out of bed, chairs, bathtub, etc., or using stairs. bUsing telephone, accessing mail, operating locks . •p < 0.05 .•• p < 0.01 .••• p < 0.001.
540-0ctober
1990
viduals were compensated $25 for participating in the study. Method Two research assistants used portable videocassette recorders and color cameras to record each participant's movements. Persons were videotaped in their homes, at the laundromat, and in the grocery store; taping took two days in most cases. Task analysis was accomplished using a program that links a videotape player to a personal computer using a BCD videolink. The task analysis software written for this project controls the videotape player from the computer keyboard. It is menu driven, and the selection of tasks, actions, demands, objects, and locations is standardized. In addition, the program records frame numbers on the videotape itself so that specific sequences of tasks can be accurately and easily accessed for more detailed coding or reliability checks (Weber, Czaja, and Redmond, 1988). The task analysis coding system is a hierarchical decomposition by which tasks are first divided into component activities and then these activities are specified in terms of environmental demands. The environmental demands are translated into person actions. The location of the object to be acted on during task performance determines the posture assumed while performing the task; the object itself defines the grip assumed by the hand during the operation; and the function of the environmental demand (e.g., open, close, reposition) determines the necessary action performed to accomplish the demand (e.g., push, pull, lift, or lower). For example, the person actions associated with the task of removing a can of vegetables from a low shelf in the grocery store would be a bending posture to access the can, a palm grip around the can, and a lift to reposition the can (remove
HUMAN
FACTORS
from the shelf). Actions, postures, grips, ob· jects, and locations were coded for each task activity. Across all 25 ADL tasks 21 586 actions were coded. Table 2 is a breakdown of the number of actions per task and the number of subjects whose actions were coded. For purposes of analysis, observations were weighted according to the number of videotapes (subjects) coded for each task. Environmental measures were taken of the homes of a subsample of 47 of the individuals who had been videotaped performing the ADLs. Measurements included room dimensions and height of light switches, doorknobs, countertops, appliance tops, shelves, and so on. An inventory was also taken of the pieces of furniture and the types of appliances, locks, lamp switches, cabinet handles, and so on, in each room. The force to open doors, turn on faucets, and activate appliances was measured, as was the distance to mail, laundry, and trash facilities. Size of appliances and furniture was calculated, as was the amount offree floor space. Measurements were taken in all rooms of the dwelling (kitchen, bathroom, dining room, living room, and bedrooms). In addition, all the objects handled during task performance (door handles, appliance knobs, food containers, laundry baskets, cooking utensils, vacuum cleaners, etc.) were weighed and measured. To date more than 500 objects have been measured. RESULTS AND DISCUSSION The raw data from the task analysis of the videotapes was used to generate task demand profiles for each of the 25 tasks. A demand profile (see Figure 6 for an example) provides a detailed description of task activities and the physiological performance demands inherent in these activities. Once generated, these profiles can be analyzed in a number of
October 1990--541
ADL TASK PROFILES
TABLE 2 Demand-Action Observations by Task and Subjects ADUTask Bathing Bed changing Bed ingress/egress Brushing hair Chair ingress/egress Cleaning appliances Cleaning bathroom Communicating Dressing/undressing Dusting Floor scrubbing Floor sweeping Taking out garbage Grocery shopping Doing laundry Turning lights on/off Operating locks Mailing a letter Preparing a meal Medicine taking Brushing teeth Toileting Activating TV/radio Vacuuming Washing dishes
Observations
Subjects
601 541 108 371 107 664 670 217 362 491 1132 606 116 8339 3566
14 5
5 13
13 6 5 19 5 6 5 5 5
22 16
94
18
87
9 11 4
101 612 242 484
80 149 1142 704
ways depending on the goal of the analysis. For example, the data may be analyzed across all tasks or wi thin a task focusing on specific task demands. In this paper examples of four different approaches to using demand profiles are presented: global task descriptions, task component profiles, task-specific profiles, and action profiles. The intent is to demonstrate that by using human factors techniques to analyze daily task performance, a very rich data base is produced that can be examined in a variety of ways. The appropriate analytic approach depends on the desired outcome. For example, using these data to generate housing or product designs would require an analysis different from one using the data to predict the functional performance of frail elderly.
11
6 22 18
5 4
Observations per Subject (SD) 42.9 (26.3) 108.2 (137.3) 21.6 (14.1) 28.5 (12.3) 8.2 (4.9) 110.7 (127.3) 134.0 (96.2) 11.4 (7.4) 72.4 (92.2) 81.8 (49.1) 226.4 (199.0) 121.2 (120.4) 23.2 (13.6) 379.0 (350.3) 222.9 (155.9) 5.2 (8.1) 9.7(5.1) 9.2 (3.4) 153.0 (52.7) 22.0 (19.3) 80.7 (47.0) 3.6 (3.8)
8.3 (6.4) 228.4 (121.7) 176.0 (163.3)
Global Task Descriptions Global task descriptions specify the performance demands that are inherent in all ADL tasks. Figures 1-5 present frequency histograms for these global demands. As shown in Figure I, lift/lower and push/pull movements are the most frequent motions performed across all tasks (together they account for 60% of all actions). With regard to grips and postures (Figures 2 and 3), precision grips are used most often (40% of all hand usage), and the standard work posture (48%), lean reach (21%), and bend (14%) accounted for 83% of all postures. Of the tasks, 95% involve the use of the upper body-that is, hands and/or arms (Figure 4). Chi-square analyses suggest that it is rea-
542-0ctober 1990
HUMAN
FACTORS
50% 1. Lift/Lower 2. Push/Pull 40% 36.8%
3. Hold, Carry, Suspend 4. Rotate 5. Side to Side
30%
6. Hand to Hand 7. Fold 8. Look
20%
9. Drop or Throw 10. Reach
10%
11.Other
0% 2
3
4
5
6
8
7
9
10
11
Figure 1. Actions across all tasks.
sonable to sum across ADL tasks to secure information on actions (motions), body part (specifically grip), and posture because actions, grips, and postures are independent of ADL task (ps < 0.05). However, the location where the action takes place and the objects acted on are task dependent (ps > 0.05). For
example, during meal preparation the most frequently handled objects are food containers (for instance, jars, cans, boxes, and cooking utensils, constituting 49%), whereas while doing laundry the most frequently handled objects are clothing and bed linens (63%). A summary of the most frequently
50% 1. No Grip
42.1%
2. Precision
40%
3. Power 4. Palm 5. Cradle
30%
20%
10% 2.39%
0% 2
3
4
Figure 2. Hand grips across all tasks.
5
October 1990---543
ADL TASK PROFILES
1. Standard
Work
2. Lean Reach 3. Bend 4. Sit 5. High Reach 6. Stoop 7. Extra
High Reach
8. Other
0.50/.
5
6
8
7
Figure 3. Postures across all tasks. handled objects across all tasks is presented in Figure 5. The data from this analysis suggest that there are task components that are common to most ADL and IADL activities. This implies that ADL tasks do not have to be considered as independent activities, which is an
important finding with respect to the issue of assessment. Because there are performance demands common to ADL and IADL tasks, an assessment protocol that incorporates these common demands might be able to successfully predict level of ADL and !ADL impairment. For example, the data suggest that if a
50% 43.5%
1. Right
Hand
2. Both Hands 3. Left
Hand
4. Arm(s) 5. Leg(s) 6. Other
4.5%
2.1% 4 2 3 Figure 4. Body parts across all tasks.
2.1% 5
6
HUMAN
544-0ctober 1990
FACTORS
50%
1. Grocery Item 2. Laundry 40%
41%
3. Cleaning Cloth, Sponge, etc. 4. Cleaning Supply 5. Door
30%
6. Bedding 7. Money 8. Mop or Broom
20%
9. Purse or Wallet 13.4% 13.3%
10. Storage
Container
5
7
10%
0% 2
3
4
6
8
9
10
11
Figure 5. Objects across all tasks.
person is able to lift and lower, push and pull, assume a precision grip, stand for extended periods, and reach from a relatively small angle, then that person should be able to complete most routine tasks. By supplementing the task analysis data with environmental and object measures (e.g., object weight, shelf height), a more specific assessment tool for ADL task performance can be constructed. For example, a person must be able to reach to a certain height, lift objects of a certain weight, and pull with a certain force to meet the demands of the specific objects/environments used in the various tasks (e.g., removing laundry from a washer). How well global task demands are met will determine the degree of independence and identify the need for assistance. Task Components Profiles
A similar analytic approach involves summing data across all ADL tasks for groups of similar task components-for example,
grouping types of handling characteristics across tasks. All motions can be grouped as either transport tasks (moving an object from one place to another), complex manipulation tasks (motions involving movement of the fingers, hands, wrist, and arm in several planes), or gross manipulation tasks (those involving simple and sometimes repetitive movement within a single plane of motion). Our data, across all ADL tasks, show that 37% of the demands required the transport of an object without acting on it-for example, retrieving a grocery item from a shelf or moving laundry from the washer to the dryer; 16% of the demands required some sort of fine hand manipulation such as chopping a vegetable, inserting a key, or turning an appliance off or on; and 51% were of an intermediate level requiring more gross manipulation, such as pushing a vacuum cleaner, wiping a counter, or folding laundry. This analysis approach can be used to identify and prioritize needed areas of intervention and suggest areas where human factors
October 1990-545
ADL TASK PROFILES
engineering can be applied to reduce task demands. For example, the data suggest that assistance with transport movements might have a major impact on ADL performance. Examination of objective dimensions of transport tasks will suggest ways in which the design of products or environments may be altered to better meet the capabilities of older people. For example, storage locations may be better placed to reduce long-distance transport of heavy objects such as vacuum cleaners. Lighter-weight products could be developed for objects transported with high frequency, and handle dimensions could be changed to allow better control through the use of more effective grip types. Assistive technologies, including movable storage units or a track-mounted robotic arm, could be designed and installed to complete or assist frail elderly with these transport movements. Given the increased incidence of osteoarthritis of the hand in the older population (Plato and Norris, 1979), attention to specific task parameters associated with transporting objects could greatly improve design of assistive technology and enhance daily living for a great many older persons. Grouped task component data can also be used to generically assess the effect of specific disabilities on daily task performance and target interventions for afflicted individuals. For example, persons with osteoarthritis or bursitis in the shoulder have difficulty performing a large number of tasks because transporting objects is a component of most tasks and is difficult to perform with limited shoulder mobility. Persons with these disabilities would benefit most by technologies designed to assist with transport tasks. A person with limited finger and hand flexibility would require more complex assistive technology or intensive support services because both manipulation tasks and transport tasks would be difficult, given the handling de-
mands of these tasks. Information of this type would be useful for care givers who provide support services to older persons. Task-Specific Profiles An additional analytic approach is to examine individual ADL tasks. For this study, task demand profiles were generated for each of the 25 specific ADL tasks. A task-specific approach is desirable when the interest is in designing or assisting task performance within a specific environment or for a specific task. For example, a grocer's association would be interested in task profiles only for grocery shopping, not for housecleaning or doing laundry, whereas a human factors engineer interested in generating guidelines for eldery housing would be interested in data associated with tasks such as meal preparation or bathing/showering. Figure 6 presents a component map for one ADL-changing bed linens-to illustrate the efficacy of this approach. Most older persons perform this task on a weekly basis and report it as problematic. The most frequently reported problem with bed changing is the bending and leaning movements it requires. This becomes an obvious prediction when examining the component data. To change bed linens, one must operate at a work height (bed height) of 55.88 em, which requires bending postures 49% of the time and leanreach postures 26% of the time. Lift/lower (29%) and push/pull (23%) are the most frequent actions performed. It is clear that a simple redesign involving raising the height of the bed could greatly reduce the problematic demands associated with changing bed linens. However, raising the height of the bed may make it difficult for older people to get in and out of bed and create a potential for fall accidents. A bed with easily adjustable height might be the best alternative. Such a device has been designed
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