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The impact of near visual impairment on instrumental activities of daily living among community-dwelling older adults in Selangor

Abstract

Objective

Near visual impairment (VI) is a common disability in an aging population. Near vision is crucial in activity of daily living including reading, smartphone and computer use and meal preparation. This study was conducted to determine the association between near visual acuity (VA) and contrast sensitivity (CS) with activity of daily living (ADL) among visually impaired older adults.

Results

A total of 208 participants aged  ≥  60 were recruited from the population-based longitudinal study on neuroprotective model for healthy longevity. Habitual near VA and CS were measured using Lighthouse near VA chart and Pelli-Robson CS chart, respectively. Lawton instrumental activities of daily living (IADL) was used to assess ADL. There are 41.8% participants with near visual impairment and 28.7% among them had IADL disability. Independent t test showed significant lower mean IADL score among visually impaired participants [t(206)  =  2.03, p  =  0.04]. IADL score significantly correlated with near VA (r  =   − 0.21, p  =  0.05) but not with CS (r  =   − 0.14, p = 0.21). Near VA (B  =   − 0.44, p  =  0.03) and age (B  =   − 0.07, p  =  0.01) significantly predicted IADL. The findings show poorer VA renders higher IADL disability, which may necessitate interventions to improve ADL among visually impaired older adults.

Introduction

Visual impairment (VI) is a common disability among older adults worldwide and its prevalence increases with advancing age [1]. Globally, the prevalence of VI reported was 7.7% in which 64.2% from it was among population aged  ≥  50 and 419 million older adults had near VI due to uncorrected presbyopia [2]. Most of the studies focus on distance VI and often ignore near VI as an important aspect of visual disability [3,4,5,6,7]. Good near vision is crucial in daily activities including reading, digital devices usage and preparing meal [8].

Activity of daily living (ADL) refers to the fundamental skills necessary for daily self-care which further categorized into basic ADL (BADL) and instrumental ADL (IADL) [9]. BADL is functional skills that are mastered early in life, including feeding, personal hygiene, dressing, ambulating, continence, and toileting. IADL involves more complex thinking and organizational skills such as housekeeping, managing finances, handling medications and meal preparation [10]. Significant association between VI and IADL limitation but not ADL limitation was found as ADL involved automatic tasks that learned through repetitive practices, requiring less cognitive and visual skills [11, 12]. A high prevalence of IADL limitation among Malaysian older adults (42.5–58.1%) was reported but the effect of poor near vision on IADL remain unclear as vision was not assessed [13,14,15].

VI led to person-environment misfit, causing difficulty in handling daily tasks even at familiar environment [16]. There were 18.9% older adults with near VI having IADL limitation and a higher prevalence (27.6%) among those receiving home care in Ontario, Canada [17, 18]. Ishihara et al. [19] suggested the importance of contrast sensitivity (CS) in handling of small things (e.g., coins, telephone, medicine), perception of step edges and detection of obstacles among elderly. Hence, further investigation on the relationship between CS and IADL was suggested [11, 18, 20]. Currently, evidence on the association between near visual acuity (VA) and CS with IADL among community-dwelling older adults in Malaysia is still lacking. Research conducted overseas may not be applicable to the Malaysian context as IADL can be influenced by environmental, societal, and cultural factors [12, 13, 18, 21]. As the Malaysian elderly population is expanding following improved healthcare promoting longevity [7, 22, 23], it was estimated that VI and physical disability will increase concurrently. We hypothesized that near VA and CS are associated with IADL score among older adults. Hence, this study was conducted to determine the association between near VA and CS with IADL among visually impaired older adults in Selangor, Malaysia.

Main text

Methods

Participants were recruited from 12 places randomly selected from Selangor state (Kuala Langat, Kajang, Tanjung Sepat, Sungai Pelek, Tanjung Karang, Kuala Selangor, Sekinchan, Keramat, Klang, Petaling Jaya, Kelana Jaya and Batu 9 Cheras) from August 2018 to May 2019 and were analyzed cross-sectionally. Sample size, n0 was calculated based on Cochran formula [25]:

$${\text{n}}_{{0}} {\text{ = Z}}^{{2}} {\text{pq/e}}^{{2}}$$

where Z2 is 1.96 for 95% confidence interval, p, estimated proportion is 2.04% IADL limitation among VI population [18], q is 1 − p and e, precision level is 0.05. The total sample size required was 40 after added with 20% drop out.

Inclusion criteria were older adults aged  ≥  60 and without documented major psychiatric illnesses or mental disorders. Those with Mini-Mental State Examination (MMSE) score  ≤  14, indicating moderately severe or severe cognitive impairment were excluded [26]. This study adhered to the Declaration of Helsinki and was approved by the Medical Research and Ethics Committee of Universiti Kebangsaan Malaysia (UKM1.21.3/244/NN-2018-145).

A total of 230 participants agreed to participate and signed informed consent was obtained. After excluding 22 participants due to missing data or MMSE score  ≤  14, the sample size of 208 participants remained. Participants were interviewed on demographic information including age, gender, races, and educational level.

Assessment

Habitual near VA was measured monocularly at 40 cm using Lighthouse near VA chart (Precision Vision, USA). The smallest lines of the chart that participants able to read was recorded in M unit and  ≤  0.8 M was defined as no VI whereas  >  0.8 M as VI [27]. The lower M score in Lighthouse near chart indicates better near VA.

CS was measured binocularly using Pelli-Robson Contrast Sensitivity chart at 1 m with chart luminance of 85 cd/m2 [28]. The lowest triplet of letters with at least two of the three letters read correctly was recorded as log CS.

Malay version Lawton IADL was administered to assess independent living skills [29]. IADL questionnaire assessed for seven items including phone usage, shopping, doing housework, finance management, traveling, food preparation and taking medications [24]. Each item was scored as 0 (not able/dependent to perform task), 1 (perform task with assistance) or 2 (perform task independently). The total IADL score is 14, in which lower IADL score showed severe IADL disability was defined as assistance needed for the task or not able to do the task at all [30].

Malay version of MMSE for visually impaired (M-MMSE-blind) was used to assess cognitive function [31]. The score for M-MMSE-blind was calculated by eliminating items involving vision (naming, performing a three-stage command, following a written instruction, writing a sentence, and copying), leaving a total score of 22.

Statistical analysis

All statistical analyses were conducted through IBM SPSS Statistics for Windows, version 23.0 (IBM Corp., Armonk, N.Y., USA). Descriptive statistics was used to present the mean and standard deviation for continuous data whereas frequency and percentage for categorical data. The data was normally distributed (p  >  0.05). Mean IADL score between VI and non-VI groups were compared using independent t test. Correlations between near VA and CS with IADL score was determined with Pearson correlation. An entry criterion of p  <  0.20 was used in simple linear regression to determine the association between near VA and CS with IADL and significance was determined at p  <  0.05 level [32].

Results

From a total of 208 participants with mean age of 72.39  ±  5.33, 41.8% had near VI (Table 1). Among VI participants, female (63.2%) slightly outnumbered male (36.8%) and majority are Chinese (70.1%). Most of them received primary (34.5%) and secondary education (39.1%). The results also show that most of the participants have more than one health problem. About one third with diabetes mellitus (34.5%) and osteoarthritis (32.2%) whereas about half with hypertension (52.9%) and high cholesterol (51.7%).

Table 1 Characteristic and clinical assessments of all participants according to VI status

The mean of near VA, CS, M-MMSE-blind score and IADL score among VI participants were 1.42  ±  0.65 M, 1.44  ±  0.21 Log CS, 17.99  ±  3.16 and 13.36  ±  1.32, respectively. Among the participants with near VI, 28.7% (n  =  25) had IADL disability, and 71.3% (n  =  62) had no IADL disability. For participants without near VI, 17.4% (n  =  21) had IADL disability, and 82.6% (n  =  100) had no IADL disability. Independent t test showed significant lower IADL score in VI group as compared to non-VI group [t(206)  =  2.03, p  =  0.04].

Among VI group, independent t-test revealed no significant different in mean IADL score among gender [t(85)  =  0.75, p  =  0.46] (Table 2). ANOVA test showed no significant different in mean IADL score among different races [F(2,84)  =  0.12, p  =  0.89] and educational level [F(3,83)  =  2.15, p  =  0.10].

Table 2 IADL score stratified by gender, races, and educational level among VI participants (n  =  87)

Pearson correlation showed higher IADL score (less disability) significantly correlated with better near VA (lower M score in Lighthouse near chart) (r =  − 0.21, p  =  0.05), but not with CS (r  =  − 0.14, p = 0.21) (Table 3).

Table 3 Pearson correlation between age, cognition and visual function with IADL among VI participants (n  =  87)

A multiple linear regression was conducted to predict IADL score based on near VA, age and M-MMSE-blind score among VI group. A significant regression equation was found [F(3,83)  =  5.37, p  <  0.01], with R2 of 0.16. Participants’ predicted IADL score is equal to 17.83 − 0.44(NEAR VA, in M unit) − 0.07(AGE, in year)  +  0.06(M-MMSE-BLIND SCORE, in point). Participants’ IADL score decreased by 0.44 M unit for each of near VA, 0.07 year for each of age and 0.06 point for each of M-MMSE-blind score. Better near VA (B  =  − 0.44, p  =  0.03) and increasing age (B  =  − 0.07, p  =  0.01) were significant predictors of IADL score whereas M-MMSE-blind (p  =  0.14) did not.

Discussion

To our knowledge, this is the first study on the association between near VA and IADL among visually impaired older adults in Malaysia. The present study, conducted among community-dwelling older adults with VI, highlights a significant inverse relationship between near VA with IADL. The poorer the near VA, the more severe the IADL disability among older adults with VI. Previous studies found that self-reported poor near vision doubled the risk of IADL limitation [OR  =  2.10, 95% CI(1.52, 2.90)] [12] and the risk increased with severity of VI, from 2.2 times in mild near VI to 3.6 times in moderate to severe near VI [21]. A more recent study reported a significant association between near VI (Parinaud score  >  2) and IADL limitation both cross-sectionally [OR  =  1.60, 95% CI(1.2, 2.0)] and longitudinally [OR  =  1.2, 95% CI(1.0, 1.4)] [18]. In addition, near VI causes greater risk of developing IADL limitation in all tasks except shopping and phone usage. However, variations in near VA assessment and definitions on VI and IADL limitation across these studies limit direct comparison of the findings.

The findings of this study support previous study in which elderly with self-reported fair to poor vision experienced difficulty in any IADL activity, especially meals preparation, phone usage and money management [33]. Older adults with VI experienced reading-related barriers in all activities in IADL such as reading expiration dates, medication instructions, product labels and prices, identifying buttons on appliances and dealing with coins or bills [34]. All the tasks as previously mentioned required good visual abilities as suggested by Berger and Porell [12]. They stressed on the necessity of visual skills, fine motor dexterity, and cognitive skills especially in phone use, medication, and finance management.

Rubin et al. [35] found that CS was a significant risk factor for IADL limitation among elderly [OR  =  1.93, 95% CI(1.30, 2.87)] but the association did not persist after adjustment for age, gender, race and chronic medical conditions [OR  =  1.45, 95% CI(0.95, 2.22)] [35]. However, this study did not find any significant correlation between CS and IADL score. This may be because the mean CS (1.53  ±  0.84 log units) in this study was above the level of CS that can cause disability. West et al. [36] reported that mobility and heavily visual intensive tasks were affected when CS are worse than 0.9 log units and 1.4 log units, respectively [36].

The association between various subitems in MMSE with IADL have been commonly reported [37,38,39]. For instance, “orientation to time” in MMSE was reported to be associated with “ability to handle finances” and “responsibility for own medications” in IADL [40]. Similar to study by Safak et al. [39], our findings show significant correlation between MMSE and IADL. However, different from study by Lee et al. and Han et al. [37, 38], there is no significant association in regression analysis. Discrepancy in findings was likely due to our study having included only older adults with no measurable cognitive impairment, whereas others included those with mild cognitive impairment, dementia, and Alzheimer’s Disease.

Our study suggests that age was a significant predictor of IADL score, which agrees with previous findings [13, 41]. Aging is associated with generalized deterioration of body organs and systems, lowering effectiveness of physiological functions, which lead to a greater risk for various chronic disease and inadaptability among elderly. Another study reported an opposite trend, in which individuals with earlier onset of VI have less IADL disability as they may be equipped with skills to overcome the disability compared to those with onset of VI at older age [42].

The strengths of this study include objective measurement of near VA and classification of near VI based on ICD-11 for Mortality and Morbidity Statistics [27]. Secondly, M-MMSE-blind was used in analysis as controlling factors thus addressing the potential influence of cognitive impairment among VI subjects.

This study suggested a significant inverse association between near VA and IADL. There may be a need for interventions appropriate to the visually impaired such as optical correction and low vision rehab, to optimize IADL and mitigate disability. Concomitant public health measures may include activities to improve public awareness regarding the availability of such resources and programs in the community.

Limitations

This study did not use a specific ADL questionnaire for VI population. Further study should be conducted using a specific ADL questionnaire designed for VI population in order to gain a more specific or focused finding. As this study was only conducted in a Malaysian context, its findings might only be relevant to the Malaysian older adult’s population. It is interesting to know if same findings could be found in other countries or cultural settings.

Availability of data and materials

The author confirm that all relevant data are included in the article.

Abbreviations

VI:

Visual impairment

ADL:

Activity of daily living

BADL:

Basic activity of daily living

IADL:

Lawton instrumental activities of daily living

CS:

Contrast sensitivity

VA:

Visual acuity

M-MMSE-blind:

Malay version of Mini-Mental State Examination for visually impaired

n:

Number

SD:

Standard deviation

References

  1. 1.

    Bourne RRA, Flaxman SR, Braithwaite T, Cicinelli MV, Das A, Jonas JB, Keeffe J, Kempen JH, Leasher J, Limburg H, Naidoo K, Pesudovs K, Resnikoff S, Silvester A, Stevens GA, Tahhan N, Wong TY, Taylor HR. Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Lancet Glob Health. 2017;5:e888–97.

    PubMed  Article  Google Scholar 

  2. 2.

    GBD 2019 Blindness and Vision Impairment Collaborators. Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2020;9:e130–43.

    Google Scholar 

  3. 3.

    Varma R, Vajaranant TS, Burkemper B, Wu S, Torres M, Hsu C, Choudhury F, McKean-Cowdin R. Visual impairment and blindness in adults in the United States: demographic and geographic variations from 2015 to 2050. JAMA Ophthalmol. 2016;134:802–9.

    PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Aljied R, Aubin M-J, Buhrmann R, Sabeti S, Freeman EE. Prevalence and determinants of visual impairment in Canada: cross-sectional data from the Canadian Longitudinal Study on Aging. Can J Ophthalmol. 2018;53:291–7.

    PubMed  Article  Google Scholar 

  5. 5.

    Guo C, Wang Z, He P, Chen G, Zheng X. Prevalence, causes and social factors of visual impairment among Chinese adults: based on a National Survey. Int J Environ Res Public Health. 2017;14:1034.

    PubMed Central  Article  Google Scholar 

  6. 6.

    Chew FLM, Salowi MA, Mustari Z, Husni MA, Hussein E, Adnan TH, Ngah NF, Limburg H, Goh P-P. Estimates of visual impairment and its causes from the National Eye Survey in Malaysia (NESII). PLoS ONE. 2018;13:e0198799.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Abd Rahman MH, Kee QT, Mohammed Z, Mohamad Fadzil N, Sahar S, Ahmad M. Visual impairment among older adults in Selangor state of Malaysia: the Grand Challenge Project. J Clin Diagn Res. 2020;14:NC05–9.

    Google Scholar 

  8. 8.

    Cunha CC, Berezovsky A, Furtado JM, Ferraz NN, Fernandes AG, Muñoz S, Watanabe SS, Sacai PY, Cypel M, Mitsuhiro MH, Morales PH, Vasconcelos GC, Cohen MJ, Campos M, Cohen JM, Belfort R Jr, Salomão SR. Presbyopia and ocular conditions causing near vision impairment in older adults from the Brazilian Amazon region. Am J Ophthalmol. 2018;196:72–81.

    PubMed  Article  Google Scholar 

  9. 9.

    Mlinac ME, Feng MC. Assessment of activities of daily living, self-care, and independence. Arch Clin Neuropsychol. 2016;31:506–16.

    PubMed  Article  Google Scholar 

  10. 10.

    Edemekong PF, Bomgaars DL, Sukumaran S, Levy SB. Activities of daily living (ADLs). Treasure Island: StatPearls Publishing; 2020.

    Google Scholar 

  11. 11.

    Nael V, Peres K, Carriere I, Daien V, Scherlen A-C, Arleo A, Korobelnik J-F, Delcourt C, Helmer C. Visual impairment, undercorrected refractive errors, and activity limitations in older adults: findings from the Three-City Alienor study. Invest Ophthalmol Vis Sci. 2017;58:2359–65.

    PubMed  Article  Google Scholar 

  12. 12.

    Berger S, Porell F. The association between low vision and function. J Aging Health. 2008;20:504–25.

    PubMed  Article  Google Scholar 

  13. 13.

    Murat MF, Ibrahim Z, Adznam SNA, Chan YM. Prevalence and determinants of instrumental activities of daily living (IADL) disability among community-dwelling elderly in a semi-urban setting in Peninsular Malaysia. Malays J Nutr. 2019;25:13–25.

    Article  Google Scholar 

  14. 14.

    Nur Asyura Adznam S, Shahar S, Rahman SA, Yusof NAM, Arshad F, Yassin Z, Salleh M, Samah AA, Sakian NIM. An action research on promotion of healthy ageing and risk reduction of chronic disease: a need assessment study among rural elderly Malays, care givers and health professionals. J Nutr Health Aging. 2009;13:925–30.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Suzana S, Boon PC, Chan PP, Normah CD. Malnutrition risk and its association with appetite, functional and psychosocial status among elderly Malays in an agricultural settlement. Malays J Nutr Malays J Nutr. 2013;19:65–75.

    CAS  PubMed  Google Scholar 

  16. 16.

    Liu C-J, Brost MA, Horton VE, Kenyon SB, Mears KE. Occupational therapy interventions to improve performance of daily activities at home for older adults with low vision: a systematic review. Am J Occup Ther. 2013;67:279–87.

    PubMed  Article  Google Scholar 

  17. 17.

    Guthrie DM, Davidson JGS, Williams N, Campos J, Hunter K, Mick P, Orange JB, Pichora-Fuller MK, Phillips NA, Savundranayagam MY, Wittich W. Combined impairments in vision, hearing and cognition are associated with greater levels of functional and communication difficulties than cognitive impairment alone: analysis of interRAI data for home care and long-term care recipients in Ontario. PLoS ONE. 2018;13:e0192971.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. 18.

    Peres K, Matharan F, Daien V, Nael V, Edjolo A, Bourdel-Marchasson I, Ritchie K, Tzourio C, Delcourt C, Carriere I. Visual loss and subsequent activity limitations in the elderly: the French Three-City Cohort. Am J Public Health. 2017;107:564–9.

    PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Ishihara K, Ishihara S, Nagamachi M, Osaki H, Hiramatsu S. Independence of older adults in performing instrumental activities of daily living (IADLs) and the relation of this performance to visual abilities. Theor Issues Ergon Sci. 2004;5:198–213.

    Article  Google Scholar 

  20. 20.

    Rokicki W, Drozdzowska B, Czekajło A, Grzeszczak W, Wiktor K, Majewski W, Pluskiewicz W. Relationship between visual status and functional status and the risk of falls in women. The RAC-OST-POL Study. Arch Med Sci. 2016;12:1232–8.

    PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Grue EV, Ranhoff AH, Noro A, Finne-Soveri H, Jensdottir AB, Ljunggren G, Bucht G, Bjornson LJ, Jonsen E, Schroll M, Jonsson PV. Vision and hearing impairments and their associations with falling and loss of instrumental activities in daily living in acute hospitalized older persons in five Nordic hospitals. Scand J Caring Sci. 2009;23:635–43.

    PubMed  Article  Google Scholar 

  22. 22.

    Department of Statistics Malaysia. Current population estimates. Putrajaya: Department of Statistics Malaysia; 2018.

    Google Scholar 

  23. 23.

    Heikkinen E. What are the main risk factors for disability in old age and how can disability be prevented? Copenhagen: WHO Regional Office for Europe; 2003.

    Google Scholar 

  24. 24.

    Shahar S, Omar A, Vanoh D, Hamid TA, Mukari SZM-S, Din NC, Rajab NF, Mohammed Z, Ibrahim R, Loo WH, Meramat A, Kamaruddin MZ, Bagat MF, Razali R. Approaches in methodology for population-based longitudinal study on neuroprotective model for healthy longevity (TUA) among Malaysian Older Adults. Aging Clin Exp Res. 2016;28:1089–104.

    PubMed  Article  Google Scholar 

  25. 25.

    Singh AS, Masuku MB. Sampling techniques and determination of sample size in applied statistics research: an overview. Int J Econ Commer Manag. 2014;II:1–22.

    Google Scholar 

  26. 26.

    Malek Rivan NF, Shahar S, Rajab NF, Singh DKA, Din NC, Hazlina M, Hamid TATA. Cognitive frailty among Malaysian older adults: baseline findings from the LRGS TUA cohort study. Clin Interv Aging. 2019;14:1343–52.

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    WHO. International classification of diseases for mortality and morbidity statistics. 11th ed. Geneva: World Health Organization; 2018.

    Google Scholar 

  28. 28.

    Pelli DG, Robson JG, Wilkins AJ. The design of a new letter chart for measuring contrast sensitivity. Clin Vision Sci. 1988;2:187–99.

    Google Scholar 

  29. 29.

    Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9:179–86.

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Hochberg C, Maul E, Chan ES, Van Landingham S, Ferrucci L, Friedman DS, Ramulu PY. Association of vision loss in glaucoma and age-related macular degeneration with IADL disability. Invest Ophthalmol Vis Sci. 2012;53:3201–6.

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Reischies FM, Geiselmann B. Age-related cognitive decline and vision impairment affecting the detection of dementia syndrome in old age. Br J Psychiatry. 1997;171:449–51.

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Bendel RB, Afifi AA. Comparison of stopping rules in forward “stepwise” regression. J Am Stat Assoc. 1977;72:46–53.

    Google Scholar 

  33. 33.

    Sloan FA, Ostermann J, Brown DS, Lee PP. Effects of changes in self-reported vision on cognitive, affective, and functional status and living arrangements among the elderly. Am J Ophthalmol. 2005;140:618–27.

    PubMed  Article  Google Scholar 

  34. 34.

    Ryan EB, Anas AP, Beamer M, Bajorek S. Coping with age-related vision loss in everyday reading activities. Educ Gerontol. 2003;29:37–54.

    Article  Google Scholar 

  35. 35.

    Rubin GS, Roche KB, Prasada-Rao P, Fried LP. Visual impairment and disability in older adults. Optom Vis Sci. 1994;71:750–60.

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    West SK, Rubin GS, Broman AT, Munoz B, Bandeen-Roche K, Turano K. How does visual impairment affect performance on tasks of everyday life? The SEE Project. Arch Ophthalmol. 2002;120:774–80.

    PubMed  Article  Google Scholar 

  37. 37.

    Lee M-T, Jang Y, Chang W-Y. How do impairments in cognitive functions affect activities of daily living functions in older adults? PLoS ONE. 2019;14:e0218112.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Han G, Maruta M, Ikeda Y, Ishikawa T, Tanaka H, Koyama A, Fukuhara R, Boku S, Takebayashi M, Tabira T. Relationship between performance on the Mini-Mental State Examination sub-items and activities of daily living in patients with Alzheimer’s disease. J Clin Med. 2020. https://doi.org/10.3390/jcm9051537.

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Şafak ED, Kizilçay HD, Arguvanli S, Mazicioglu MM, Mucuk S, Öztürk A, Gocer S, Kiris Y, Akin S. Relationship between activities of daily living and cognitive function among community-dwelling elderly in urban areas of Kayseri, Turkey: a cross-sectional study. Konuralp Med J. 2019;11:30–5.

    Google Scholar 

  40. 40.

    Prakoso K, Vitriana V, Ong A. Correlation between cognitive functions and activity of daily living among post-stroke patients. Althea Med J. 2016;3:329–33.

    Article  Google Scholar 

  41. 41.

    Burman J, Sembiah S, Dasgupta A, Paul B, Pawar N, Roy A. Assessment of poor functional status and its predictors among the elderly in a rural area of West Bengal. J Midlife Health. 2019;10:123–30.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Mueller-Schotte S, Zuithoff NPA, van der Schouw YT, Schuurmans MJ, Bleijenberg N. Trajectories of limitations in instrumental activities of daily living in frail older adults with vision, hearing, or dual sensory loss. J Gerontol A Biol Sci Med Sci. 2019;74:936–42.

    PubMed  Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the contributions of the co-researchers, research assistants, enumerators, participants, their family members, community leaders and the local authorities for their cooperation throughout recruitment and data collection processes.

Funding

This study was funded by the Ministry of Higher Education Malaysia Long-Term Research Grant Scheme (LRGS/BU/2012/UKM-UKM/K/01) and Cabaran Perdana Grant Scheme (DCP-2017-002/1).

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Authors

Contributions

Conceptualization: MHAR, NMF, ZM and SS. Methodology: MHAR, NMF, ZM and SS. Formal analysis: QTK and MHAR. Investigation: QTK, MHAR, NMF and ZM. Data curation: QTK, MHAR, NMF and ZM. Writing—original draft preparation: QTK and MHAR. Writing—review and editing: QTK, MHAR, NMF, ZM and SS. Visualization: QTK and MHAR. Supervision: MHAR, NMF, ZM and SS. Project administration: MHAR, NMF, ZM and SS. Funding acquisition: MHAR, ZM and SS. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohd Harimi Abd Rahman.

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Ethics approval and consent to participate

This study adhered to the Declaration of Helsinki and was approved by the Medical Research and Ethics Committee of Universiti Kebangsaan Malaysia (UKM1.21.3/244/NN-2018-145). Signed informed consent was obtained from all participants who agreed to participate.

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Not applicable.

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The authors declare no competing interests.

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Kee, Q.T., Abd Rahman, M.H., Mohamad Fadzil, N. et al. The impact of near visual impairment on instrumental activities of daily living among community-dwelling older adults in Selangor. BMC Res Notes 14, 395 (2021). https://doi.org/10.1186/s13104-021-05813-3

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Keywords

  • Aging
  • Contrast sensitivity
  • Visual acuity
  • Visually impaired
  • Quality of life