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  2. Assessment of Quality of Life - 8 Dimensions

Assessment of Quality of Life - 8 Dimensions

Last Updated

Purpose

The Assessment of Quality of Life - 8 Dimensions (AQoL-8D) is the latest revision of a multi-attribute utility (MAU) tool to assess parameters of well-being, with an emphasis on evaluation of psychosocial elements of health.

Link to Instrument

Top page including questionnaire and scoring information

Acronym AQoL-8D

Area of Assessment

Depression
Occupational Performance
Pain
Quality of Life
Hearing
Social Relationships
General Health
Sleep
Life Participation
Activities of Daily Living

Assessment Type

Patient Reported Outcomes

Cost

Free

Actual Cost

$0.00

Cost Description

Register with the Centre for Health Economics for permission to use.

CDE Status

Not a CDE -- last searched 6/22/2023.

Populations

Musculoskeletal Conditions
Spinal Injuries
Pulmonary Diseases
Alzheimer's Disease and Progressive Dementia
Mixed Populations
Cancer

Key Descriptions

  • 35 Items
  • Minimum psychometric (unweighted) score = 35; maximum psychometric score = 175.
  • Minimum utility (weighted) score = 0; maximum utility score = 35
  • Item responses are assigned a psychometric value (A = 1, B = 2, C = 3, D = 4, E = 5) and these values are totaled; 1 indicates good quality of life and 5 indicates poor quality of life. Both psychometric and utility measures are calculated using an algorithm provided by Monash University.
  • Instrument is self-administered; it was designed to be given through the mail or in an interview.

Number of Items

35

Equipment Required

  • Instrument
  • Pen or pencil

Time to Administer

5 minutes

Required Training

No Training

Age Ranges

Adult

18 - 64

years

Elderly Adult

65 +

years

Instrument Reviewers

Mackenna Briggs, Eric Gelfand, Kowsar Mohamud, and Daphne Rich (Master of Occupational Therapy students)

Faculty mentor: Danbi Lee, PhD, OTD, OTR/L, Division of Occupational Therapy, Department of Rehabilitation Medicine, University of Washington, Seattle.

ICF Domain

Participation
Activity

Measurement Domain

Activities of Daily Living
Cognition
Emotion
General Health
Motor

Professional Association Recommendation

None found -- last searched 6/22/2023.

Considerations

  • Appropriate for most literate people and has been translated for Spanish, German, Danish, Chinese, and Italian languages.
  • AQoL-8D can discriminate between different patient subgroups (i.e. living in city vs. rural location)

Cancer

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Normative Data

Melanoma: (Dieng et al., 2018; n = 164; mean age = 58.2 (12.1))

  • Mean utility score (SD) = 0.77 (0.2)

Internal Consistency

Melanoma: (Dieng et al., 2018)

  • Excellent: Cronbach’s Alpha = 0.85

Criterion Validity (Predictive/Concurrent)

Concurrent Validity:

Melanoma: (Dieng et al., 2018)

  • Adequate concurrent validity between the overall scores on the AQoL-8D and the Functional Assessment of Cancer Treatment - Melanoma (FACT-M) (r = 0.57)
  • Excellent concurrent validity between the AQoL-8D mental health dimension and the FACT-M overall score (r = 0.61)
  • Adequate concurrent validity between the AQoL-8D mental health dimension and the FACT-M emotional well-being component (r = 0.51)

Construct Validity

Convergent Validity:

Melanoma: (Dieng et al., 2018)

  • Significant difference between reported fear of cancer recurrence inventory (FCRI) severity scores among low and high FCRI groups for both the AQoL-8D (f = 9.74, p = 0.002) and FACT-M (f = 9.98, p = 0.002)
  • Significant difference between FCRI groups in the AQoL-8D mental health dimension (f = 22.84, p < 0.001) and the emotional well-being dimension of the FACT-M (f = 38.81, p < 0.001)

Discriminant Validity:

Melanoma: (Dieng et al., 2018)

  • No significant difference between FCRI groups in the AQoL-8D super physical dimension (f = 0.04, p = 0.84) and a significant difference between the groups on the physical well-being component of the FACT-M (f = 5.69, p = 0.02)

Floor/Ceiling Effects

Melanoma: (Dieng et al., 2018)

  • Adequate ceiling effect of 0.6% and floor effect of 3.7% found

Spinal Injuries

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Normative Data

Spinal Cord Injury: (Whitehurst et al., 2016; n = 364 (63% male); able to walk n = 119; unable to walk n = 245; 1-10 years post injury n = 109, 10+ years post injury n = 255; ASIA classification A (n = 115), B (n = 31), C (n = 119), D (n = 69), E (n = 8), Uncertain (n = 22); Tetraplegia n = 175, Paraplegia n = 189; mean age = 50.40 (13.2))

Descriptive statistics for the AQoL-8D by self-reported ability to walk

 

Mean

  SD

Median

 IQR

Entire sample

(n = 364a)

 

0.573

 

0.197

 

0.561

 

0.313

Unable to walk

(n = 245)

 

0.603

 

0.194

 

0.602

 

0.323

Able to walk

(n = 119)

 

0.509

 

0.190

 

0.481

 

0.268

aOne respondent failed to complete all items.

Pulmonary Diseases

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Normative Data

Asthma: (Kaambwa et al., 2017; n = 856; Australia (n = 141), United States (n = 150), United Kingdom (n = 150), Canada (n = 138), Norway (n = 130), Germany (n = 147); age = 18+)

  • Mean (SD): 0.69 (0.20)
  • Median (IQR): 0.72 (0.54–0.86)

Construct Validity

Convergent Validity:

Asthma: (Kaambwa et al., 2017)

  • Excellent convergent validity between Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and AQoL-8D (r = 0.642)

Mixed Populations

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Test/Retest Reliability

Mixed Population: (Richardson et al., 2014; Australian sample n = 1,430, healthy public n = 265, patient n = 1,165; US sample n = 1,460, healthy public n = 321, patient n = 1,139)

  • Acceptable test-retest reliability: (ICC = .91 Base to 2-week, ICC = .89 Base to 4-week)

Internal Consistency

Mixed Population: (Richardson et al., 2014)

  • Excellent internal consistency (Cronbach’s alpha = 0.96)

Criterion Validity (Predictive/Concurrent)

Predictive Validity:

Mixed Population: (Richardson et al., 2014)

  • Adequate and at least as great as the predictive validity of other MAU instruments with an average of 43% (Australian sample) and 36% (US population) deviation from perfect prediction. Overall average deviation was 49.2% (p 94).

Construct Validity

Convergent Validity:

Mixed Population: (Richardson et al., 2014)

  • Excellent convergent validity with EuroQoL-5 Dimension (EQ-5D), Health Utilities Index 3 (HUI 3), and SF 6D (r = 0.68-0.79)
  • Adequate to excellent convergent validity with 15D (r = 0.57-0.61)
  • Adequate convergent validity with Quality of Well-Being (QWB) (r = 0.55)

Mixed Population (Richardson et al., 2016, n = 7,933; cancer (n = 772); heart disease (n = 943); other populations not reported)

  • Adequate to Excellent construct validity (r = 0.48-0.75)

Correlations of AQoL-8D With Indices of Physical and Psychosocial Content and Preferences by Patient Group

Patient Group

Physical domain

 

Psychosocial domain

Patient preference

Comparator

SF-36 physical component score (PCS)

Average of 1) SF-36 mental component score (MCS); 2)new subjective well-being (SWB) developed by UK Office of National Statistics (ONS); and 3) ICECAP, a capabilities instrument

Average of 1) Visual Analogue Scale (VAS) and 2) Self TTO (time tradeoff on own health state)

Arthritis

adequate

(r = 0.54)

              excellent

              (r = 0.74)

adequate

(r = 0.53)

Asthma

Adequate

(r = 0.49)

              excellent

              (r = 0.70)

adequate

(r = 0.54)

Cancer

adequate

(r = 0.57)

              excellent

              (r = 0.74)

adequate

(r = 0.52)

Depression

adequate

(r = 0.48)

              excellent

              (r = 0.69)

adequate

(r = 0.48)

Diabetes

adequate

(r  = 0.58)

              excellent

             (r  = 0.71)

adequate

(r = 0.52)

Hearing Loss

adequate

(r = 0.58)

              excellent

              (r = 0.72)

adequate

(r = 0.48)

Heart Disease

adequate

(r = 0.59)

              excellent

              (r = 0.75)

adequate

(r = 0.52)

 

Content Validity

Strong content validity based on comparison to the eight dimensions of the SF-36, “the most widely used non-utility measure of health- related quality of life” (p 88) and above average Pearson coefficient scores in most areas when compared with the other MAU (multi-attribute utility) instruments (Richardson et al., 2014, p. 92)

Face Validity

Assessment has high face validity as it includes items in all categories under physical and psychosocial dimensions. Richardson et al. (2014) concluded that it “has high face validity and particularly in the psychosocial dimensions, which include 24 of its 35 items.” (p. 90-91)

Floor/Ceiling Effects

Mixed Population: (Richardson et al., 2014)

  • Adequate ceiling (2.60% in Australian sample and 8.10% in US sample) and floor effects (10.60% in Australian sample and 10.10% in US sample)

Responsiveness

Mixed Population: (Richardson et al., 2016)

  • Moderate to Large effect size by patient group (Cohen’s d = 0.62 – 2.68)

 Effect Size of AQol-8D by Patient Group

Patient Group

Effect Size (Cohen’s d)

Arthritis

Positive large effect (d = 1.36)

Asthma 

Positive moderate effect (d = 0.62)

Cancer

Positive large effect (d = 1.17)

Depression

Positive large effect (d = 2.68)

Diabetes

Positive large effect (d = 1.18)

Hearing Loss

Positive moderate effect (d = 0.75)

Heart Disease

Positive large effect (d = 1.04)

Alzheimer's Disease and Progressive Dementia

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Content Validity

Dementia: (Engel et al., 2020; person with dementia n = 9; caregiver n = 17; person with dementia mean age = 74.9 , caregiver mean age = 68)

  • Study participants identified a range of aspects of quality of life they perceived as important. These were summarized into nine domains: Activity, Autonomy, Cognition, Communication, Coping, Emotions, End-of-life, Physical Functioning, and Relationships.

Face Validity

Dementia: (Engel et al., 2020)

  • Tested face validity with people with mild dementia and caregivers and reported that " the AQoL-8D (35 items) showed the greatest overlap with domains of QoL that were considered as important, although some redundancy was also noted, given it is a generic measure with some questions being less applicable to people living with dementia.”

Musculoskeletal Conditions

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Normative Data

Obesity: (Campbell et al., 2016; n = 33; mean age = 56 (11); mean time post-surgery = 6 (6) years)

Descriptive statistics for AQoL-8D utility value

Mean

0.76

Standard deviation

0.17

Median

0.81

1st to 3rd quartile (IQR)

0.63–0.88

Construct Validity

Convergent Validity:

Obesity: (Campbell, 2016)

  • Excellent correlation between the utilities for the EQ-5D-5L and AQoL-8D (r = 0.68, p < 0.001).

Floor/Ceiling Effects

Obesity: (Campbell, 2016)

  • Excellent floor and ceiling effects found (0%)

Bibliography

Campbell, J.A., Palmer, A.J., Venn, A., Sharman, M., Otahal, P., Neil, A. (2016). A head-to-head comparison of the EQ-5D-5L and AQoL-8D multi-attribute utility instruments in patients who have previously undergone bariatric surgery. Patient, 9, 311–322. https://doi.org/10.1007/s40271-015-0157-5

Dieng, M., Kasparian, N. A., Cust, A. E., Costa, D. S., Tran, A., Butow, P. N., Menzies, S. W., Mann, G. J., & Morton, R. L. (2018). Sensitivity of preference-based quality-of-life measures for economic evaluations in early-stage melanoma. JAMA Dermatology, 154(1), 52-59. https://doi.org/10.1001/jamadermatol.2017.4701

Engel, L., Bucholc, J., Mihalopoulos, C., Mulhern, B., Ratcliffe, J., Yates, M., Hanna, L. (2020). A qualitative exploration of the content and face validity of preference-based measures within the context of dementia. Health Qual Life Outcomes, 18, 178. https://hqlo.biomedcentral.com/articles/10.1186/s12955-020-01425-w

Kaambwa, B., Chen, G., Ratcliffe, J. et al. (2017). Mapping between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and five Multi-Attribute Utility Instruments (MAUIs). PharmacoEconomics, 35, 111–124. https://link.springer.com/article/10.1007/s40273-016-0446-4

Richardson, J., Iezzi, A., Khan, M.A. et al. (2014). Validity and Reliability of the Assessment of Quality of Life (AQoL)-8D Multi-Attribute Utility Instrument. Patient, 7, 85–96. https://link.springer.com/article/10.1007/s40271-013-0036-x

Richardson, J, Iezzi, Angelo, Khan, Munir A, Chen, Gang, & Maxwell, Aimee. (2016). Measuring the sensitivity and construct validity of 6 utility instruments in 7 disease areas. Medical Decision Making, 36(2), 147-159. https://journals.sagepub.com/doi/10.1177/0272989X15613522?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed

Whitehurst, D. G., Mittmann, N., Noonan, V. K., Dvorak, M. F., & Bryan, S. (2016). Health state descriptions, valuations and individuals’ capacity to walk: A comparative evaluation of preference-based instruments in the context of spinal cord injury. Quality of Life Research, 25(10), 2481–2496. https://doi.org/10.1007/s11136-016-1297-3

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