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. Author manuscript; available in PMC: 2022 Oct 1.
Published in final edited form as: Am J Ophthalmol. 2021 May 2;230:207–215. doi: 10.1016/j.ajo.2021.04.022

Environmental features contributing to falls in persons with vision impairment: The role of home lighting and home hazards

Pradeep Y Ramulu a,*, Aleksandra Mihailovic a, E Jian-Yu b, Rhonda B Miller a, Sheila K West a, Laura N Gitlin c,d, David S Friedman a,e
PMCID: PMC8560652  NIHMSID: NIHMS1723498  PMID: 33951447

Abstract

PURPOSE:

To evaluate whether home hazards and lighting levels are associated with higher fall rates in adults with varying degrees of visual field (VF) damage from glaucoma.

METHODS:

Participants with diagnosed or suspected glaucoma provided three years of prospective falls data via monthly falls diaries. Fall locations were determined via post-fall phone questionnaire. Seven home areas were evaluated for hazards and lighting via an in-home assessment. The influence of hazards and lighting on fall rates in each home region were evaluated in multivariate models adjusting for relevant confounders including age, gender, comorbidity, and severity of visual field (VF) damage.

RESULTS:

Mean baseline age for the 170 participants was 71.0 (7.6) years and 78 (46%) of participants were female. Fifty-nine participants experienced a total of 83 home falls, with the greatest number of falls occurring on the indoor stairs (n=24; 29%) and bedroom (n=17; 21%). Neither the number nor the percentage of hazardous items graded as hazardous was associated with the rate of falls (p>.26). Each 10-fold increase in room lighting was associated with 35% fewer falls in that home region (p=.02). The relation between lighting and the rate of falls did not differ with the degree of visual field damage (p>.3), and a lower fall rate was noted with better lighting even in participants with mild or no VF damage (Rate ratio=0.52 per 10-fold better lighting; p=0.01).

CONCLUSIONS:

Fewer home falls were found with better lighting, but not with fewer home hazards. Lighting improvements at home may reduce fall rates in older adults.

INTRODUCTION

Falls in older adults produce severe injuries such as fractures, psychosocial consequences such as fear of falling, loss of independence, and billions of dollars in annual healthcare expenditure.1–6 The home environment is the most common location where falls occurr.7–9 As such, multiple home assessment tools have been generated to identify home features which may increase the likelihood of falls,10–17 though few tools have been externally validated (i.e. related to lower fall rates) given the significant amount of work required to tie hazard data to prospectively-obtained fall information in which both the occurrence and the location of the fall are collected. Additionally, few instruments have adequately examined specific home features such as lighting levels objectively, and most studies have not linked home hazards specifically to fall rates in the home region where the hazard was noted. This improper assessment of risk may explain why several studies have not found an association between the number of home hazards and the risk of home falls.18,19

Prior work has demonstrated that fall occurrences are more prevalent in older adults with visual impairment, particularly those with visual field loss from conditions such as glaucoma.20–24 Given the lack of therapies to restore irreversible vision loss in glaucoma, falls occurring at home are of particular interest to these patients because the home environment could be modified to reduce fall risk.25–28 Also, previous studies have demonstrated greater difficulty with hazard perception due to reduced contrast sensitivity, visual field damage, and other characteristic features of glaucoma damage.29,30 Finally, patients with glaucoma do not appear to modify hazards from the home, as individuals with moderate or severe VF damage have a similar number of hazards, and also similar levels of lighting, in all regions of their homes as compared to patients with little to no visual field damage.31

We have previously generated and described high inter-rater reliability for the HEAVI tool, a home assessment instrument designed to evaluate hazards in the home environment of visual impaired persons.31,32 Here, we examine the importance of hazard data collected using HEAVI, testing whether: (1) more home hazards are associated with a higher rate of falling at home, and (2) if specific home hazards, particularly poor lighting, would be associated with higher rates of falls. Of note, these questions were posed using regression models in which the number of hazards, percentage of items graded as hazardous, or lighting within a given home region were matched to the number of falls occurring specifically in that home region (Figure 1). Here, we hypothesize that more hazardous home regions and worse room lighting are associated with a higher rate of falls in a population of glaucoma patients with varying degrees of VF damage.

Figure 1.

Figure 1.

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Evaluation of home features (hazards, lighting) as an exposure for falls over time in location-specific models matching fall location within the home to features of the home region where the fall occurred.

METHODS

All study procedures were approved by the Johns Hopkins institutional review board (Falls in Glaucoma Study, IRB NA_00088337), and recruited participants signed written informed consent. The study adhered to the tenets of the Declaration of Helsinki.

STUDY PARTICIPANTS

We recruited study participants from the Johns Hopkins Wilmer Eye institute glaucoma service between 2013 and 2015. Subjects were at least 60 years of age at the end of the three-year study period and had a clinical diagnosis of glaucoma or suspected glaucoma (i.e. ocular hypertension, narrow angles, a positive family history, or borderline findings). We excluded patients with glaucoma due to secondary causes with systemic implications (i.e. neovascular or uveitic glaucoma). Individuals were also excluded if they had best-corrected visual acuity (VA) worse than 20/40 in either eye for reasons other than glaucoma, had been hospitalized in the last month, or had any ocular or non-ocular surgery within the last two months.

VISUAL ASSESSMENT

Visual field testing (Carl Zeiss Meditec, Carlsbad, CA) using the Standard 24–2 Swedish Interactive Testing Algorithm was performed on both eyes at the baseline study assessment, or at a recent clinical visit (median of 2.4 months between visual field testing and baseline assessment, IQR=0.7 to 6.5). Pointwise sensitivities from each eye were combined to generate a sensitivity at each spatial coordinate using the maximum sensitivity approach, and then to calculate average sensitivity across this integrated visual field as previously described.33 Normal integrated visual field sensitivity is 31 dB, with lower sensitivities suggesting visual field damage. Integrated visual field sensitivities were used to categorize participants as having mild (>28 dB), moderate (23 to 28 dB) or severe visual field damage (<23 dB), corresponding with published criteria for disease severity.34 We evaluated baseline VA with a backlit Early Treatment of Diabetic Retinopathy Study (ETDRS) chart at 4 meters. Contrast sensitivity was tested using the MARS chart (Mars Perceptrix, Chappaqua, NY).

HOME HAZARD ASSESSMENT

The Home Environment Assessment for the Visually Impaired (HEAVI) tool was used to evaluate hazards inside the homes of 174 participants as previously described.32 We instructed study participants to not tidy any rooms prior to their home evaluation. For lighting measurements, participants were asked to set room lights and window coverings to reflect typical conditions when in use. Light intensity was measured in lux using a digital light meter (Dr. Meter model LX1330B). Home visits were conducted year-round by a single trained evaluator (RM) and restricted to daytime hours. Home assessments were made subsequent to the baseline study visit, during the period of prospective falls assessment, with a median time of 9.4 months (IQR=5.1 to 11.8) separating the baseline clinic assessment and the home visit.

FALL ASSESSMENT AND FOLLOW-UP

Falls were defined for participants as unintentionally coming to rest on a ground or a lower level and illustrated using a validated instructional video.35 Participants were provided with 36 months of falls calendars after their baseline in-clinic assessment, and asked to mark their calendars daily over this 3-year period to indicate the occurrence (or absence) of a fall for each study day. Calendar data were returned monthly via mail or email. Individuals not returning calendars were contacted by phone and/or email until data were obtained or a period greater than three months passed, at which time data were recorded as missing.

Reported falls triggered a follow-up questionnaire to determine the location of the fall. Falls were defined as “in the home” if the fall occurred in one of the seven areas where the home environment was evaluated: stairs, bathroom, bedroom, dining room, hallway, kitchen and living room. Ten individuals changed residence after their first year in the study and 4 changed after their second year in the study; falls data post any residence change were excluded.

ASSESSMENT OF COVARIATES

Demographic data (age, race and gender) were gathered using standardized questionnaires. Patients were asked if they had been diagnosed with 15 comorbid illnesses,41 and total illnesses were summed to generate a comorbid index. Seven individuals (4%) had greater than 5 illnesses and these individuals were reclassified as having 5 comorbid illnesses. Medication bottles were observed directly to calculate the number of medications, or information was collected via questionnaire when necessary. Polypharmacy was defined as the use of 5 or more prescribed non-eye drop medications.42

STATISTICAL ANALYSIS

For each participant, falls were tabulated for each home region and for all graded regions of the home (stairs, bathroom, bedroom, dining room, hallway, kitchen and living room). Between four and seven regions were graded for study homes (all seven regions in 45 homes [27%]; 6 regions in 88 homes [52%], 5 regions in 26 homes [15%] and 4 regions in 11 homes [6%]) either because the home did not contain one or more rooms, or because the participant reported not using that region of their home. Falls for which no falls follow-up questionnaire data were available, or which occurred outside of graded areas (44 falls) were not included in regression models as they could not be matched to hazard/lighting data.

For each home region and the full home (all graded regions), hazards were described as the total number of hazards in those region(s). As some homes/home regions may demonstrate more hazards because they had more potentially hazardous items, the percentage of items graded as hazardous was also evaluated. For the full home, average lighting at home was calculated based on the light levels measured in all graded regions.

Separate negative binomial models were first used to determine the associations between the total number or frequency of home hazards, or average lighting at home, with the rate of home falls over time (falls/year). The number of falls occurring at home was the outcome of interest, and total study time in years was used as the offset (rate denominator).

Subsequent analyses treated each home region of an individual as an observation, using negative binomial regression with generalized estimating equations (GEE) to account for clustering between the different areas of the home within the same individual, and matching hazard evaluation and lighting to the specific room in which falls did or didn’t occur (Figure 1). All models controlled for age, race, sex, comorbidities, polypharmacy, and integrated visual field sensitivity. In models where relationships between hazards/lighting and fall rates were noted, additional analyses (formal tests of interaction, stratified analyses) were conducted to test the importance of severity of visual field damage on the relationship of hazards/lighting and fall rates.

RESULTS

DESCRIPTION OF STUDY PARTICIPANTS

Out of 245 participants recruited into the Falls In Glaucoma Study 174 (71%) completed the home assessment component. Participants who did not complete the home assessment did not differ from those who did with respect to age, race, gender, number of comorbidities, polypharmacy or integrated visual field sensitivity (p>.05). A small number of participants with home assessment data were excluded from the analysis based on the presence of excessive falls (>20), presumably from an undiagnosed neurological condition (n=2) or because of unavailable fall data (n=2), leaving a study population of 170 individuals.

Study participants had a mean age of 71 (SD=7.6) years; 78 (46%) were female, 51 (30%) African-American, 105 (62%) had more than one comorbid illness and 50 (29%) used 5 or more prescription medications (Table 1). Median integrated visual field sensitivity was 28.0 dB (normal value = 31 dB, interquartile range [IQR] = 25.9 to 29.7 dB), while median better and worse-eye mean deviations were −2.5 dB [IQR=−5.4 to −0.7 dB] and −5.7 dB [IQR=−13.4 to −2.6 dB], respectively. Average follow-up duration was 31.4 months (range: 7 to 36). Fifty-nine (35%) individuals fell at least once within the evaluated home regions for a total of 83 falls. Most home falls occurred on the indoor stairs (n=24, 29%), in the bedroom (n=17, 21%), in the living room (n=13, 16%) and in the kitchen (n=12, 15%) (Figure 2).

Table 1.

Characteristics of Falls in Glaucoma Study participants providing home assessment and falls data.

Demographics Values (n=170)
Age (years), mean (SD) 71.0 (7.6)
African-American race, n (%) 51 (30)
Female gender, n (%) 78 (46)
Employed, n (%) 56 (33)
Lives alone, n (%) 32 (19)
Education, N (%)
Less than high school 6 (4)
High school 18 (10)
Some college 22 (13)
Bachelor’s degree 44 (26)
More than bachelor’s degree 80 (47)
Health
Comorbid illnesses > 1, n (%) 105 (62)
Polypharmacy, n (%) 50 (29)
Body Mass Index (kg/m^2), mean (SD) 27.3 (5.0)
Grip strength (kg), mean (SD) 32.1 (10.4)
Lower body strength (kg), mean (SD) 18.0 (6.4)
Vision
IVF sensitivity (dB), median (IQR) 28.0 (25.86, 29.73)
MD better-eye, median (IQR) −2.50 (−5.38, −0.68)
MD worse-eye, median (IQR) −5.69 (−13.35, −2.62)
Better-eye acuity-logMAR, median (IQR) 0.06 (−0.02, 0.16)
Binocular log CS, median (IQR) 1.72 (1.64, 1.76)
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SD=standard deviation, n=number, kg=kilogram, m=meter, IVF=integrated visual field, dB=decibel, IQR=interquartile range, MD=mean deviation, logMAR=logarithm of the minimum angle of resolution, CS=contrast sensitivity.

Figure 2.

Figure 2.

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Number and relative rate of falls per year by home region. In calculations of relative rate of falls (top row of values), stairs were used as a reference group. First number represents the rate ratio and in the brackets are the 95% confidence intervals for the rate ratio estimate. The second row of values reflects the number of falls in that home region, with the percentage (with respect to total analyzed falls) included in parentheses.

FREQUENCY OF FALLS AND LIGHTING IN VARIOUS HOME REGIONS

In univariate models, fall rates were highest on the stairs, with lower rates observed in the bathroom (RR=0.20, 95% CI=0.08 to 0.50, p=.001), dining room (RR=0.24, 95% CI=0.10 to 0.56, p=.001), hallway (RR=0.15, 95% CI=0.05 to 0.44, p=.001), kitchen (RR=0.40, 95% CI=0.19 to 0.82, p=.01), and the living room (RR=0.43, 95% CI=0.21 to 0.87, p=.02). There was no significant difference in rate of falls between the indoor stairs and the bedroom (p=.1), though the trend was towards fewer falls in the bedroom as compared to the stairs (Figure 2).

In univariate models, we noted that lighting was significantly better in several regions of the home as compared to the stairs (bathroom: RR=2.34, 95% CI=2.09 to 2.63, p<.001; bedroom: RR=1.36, 95% CI=1.21 to 1.53, p<.001; dining room: RR=2.29, 95% CI=2.04 to 2.58, p<.001, living room: RR=1.59, 95% CI=1.39 to 1.83, p<.001, and kitchen: RR=2.11, 95% CI=1.88 to 2.36, p<.001). There was no significant difference in lighting between the indoor stairs and the hallway (p=.88) (Figure 3).

Figure 3.

Figure 3.

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Relative lighting levels and percentage of homes meeting recommended lighting levels across various home regions. Relative light levels (top row of data for each room) for each home region were calculated relative to stair lighting. The number and percentage of homes meeting recommended lighting levels is provided in the second row of data for each room.

Recommended minimum lighting levels set by the Illuminating Engineering Society of North America (IESNA) is generally around 30 footcandles (323 lux), and the home regions least likely to be at this level of lighting included the hallway (11 homes, 8% of graded hallways), bedroom (13 homes, 8% of graded bedrooms), and stairs (14 homes, 11% of graded stairs) (Figure 3)

TOTAL HAZARDS, AVERAGE HOME LIGHTING, AND OVERALL HOME FALL RATES

Neither the total number of home hazards nor the overall percentage of items graded as hazardous within the full home were associated with the rate of falls within the home (Rate ratio [RR]=1.05; 95% CI= 0.73 to 1.51; p=.80 and RR=1.03; 95% CI= 0.75 to 1.42; p=.84, respectively). Likewise, average lighting across graded home regions showed no associations with the rate of falls within the home (RR=0.94; p=.88).

ROOM-LEVEL HAZARDS AND LIGHTING AND RATES OF FALLING IN A PARTICULAR ROOM

The distribution of lighting, hazard number, and proportion of items graded as hazardous is shown for rooms in which falls were not reported by the participant, noted once for a given participant, or noted more than once for a given participant (Figure 4).

Figure 4.

Figure 4.

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Distribution of number of home hazards, proportion of home features graded as hazardous, and lighting across various regions of the home for study participants. Participants experiencing no falls in that home region are shown as grey circles while those experiencing 1 fall or more than one fall are shown in black and white circles, respectively.

In separate models where fall rates were modeled for each room of each participant (accounting for within-person correlations across rooms of the home), neither the number of hazards (RR=1.18 per 10 additional hazards; 95% CI= 0.46 to 3.05; p=.73) nor the percentage of items graded as hazardous (RR=0.92 per 10% increment in items graded as hazardous; 95% CI= 0.79 to 1.07; p=.26) were associated with a higher rate of falling in a particular room. However, in an additional model in which only lighting was considered, each 10-fold increment in lighting was associated with a 35% lower rates of falls per year (RR=0.65, 95% CI= 0.46 to 0.92, p=.02) (Table 2). Home regions with lighting levels above the lighting level of 323 lux (30 footcandles) had a 50% lower rate of falls (RR=0.50, 95% CI= 0.26 to 0.96, p=.04) as compared to rooms below this lighting threshold. Data were used to model the expected rate of falls per year within a given home region by the level of home lighting within that region (Figure 3), assuming centered values for all covariates.

Table 2.

Association between the number of room hazards, percentage of room features graded as hazardous, room lighting, and person features with room-specific fall rates.

Hazards Falls/year RR (95%CI)
Total number of hazards (10 more hazards) 1.18 (0.46 – 3.05)
% features graded as hazardous (10% more hazards) 0.92 (0.79 – 1.07)
Lighting (1 log10 increment in lux) 0.65 (0.46 – 0.92)
Covariates
Age/5 years increase* 1.15 (0.98 – 1.34)
Male (vs. Female)* 0.80 (0.50 – 1.27)
African-America (vs. Other)* 0.93 (0.54 – 1.60)
IVF sensitivity/5dB worse* 1.00 (0.73 – 1.36)
Number of comorbidities* 1.18 (0.99 – 1.41)
Polypharmacy (>=5 vs. <5)* 1.39 (0.80 – 2.42)
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Three hazard-related variables were explored in relation to falls in separate regression models. Models controlled for age, race, gender, IVF sensitivity, number of comorbidities and polypharmacy.

*

Covariates present are from the model that included lighting as an exposure.

Bolded is a statistically significant result. RR-rate ratio, CI-confidence interval, lux-unit of illuminance, dB-decibels.

VISUAL FIELD DAMAGE AND THE RELATIONSHIP BETWEEN LIGHTING AND FALLS

The effect of poor lighting on the rate of falls did not differ with the degree of visual filed damage from glaucoma (p>.3). Fall rate decreased by 48% with 10-fold better lighting among the individuals with the mild to no visual filed loss from glaucoma (RR=0.52 per 10-fold better lighting; 95% CI= 0.31 to 0.87; p=.01). There was no significant relationship between fall rates and better lighting among the individuals with moderate/severe visual field damage from glaucoma (RR=0.77 per 10-fold better lighting; 95% CI= 0.48 to 1.24; p=.29), although the trend was in the direction of reduced rate of falls with better lighting for this group as well.

DISCUSSION

In this population of individuals with a varying degree of visual impairment (visual field damage), neither the number of home hazards nor the percentage of home items graded as potential fall hazardous was associated with a higher rate of falls. Dimmer lighting, on the other hand, was associated with a higher rate of falls, and the association of lighting with fall rates appeared consistent across the degree of visual field damage observed, suggesting that poor lighting may also be a risk factor for falls in all older adults. Our findings point to a possible important role of lighting with respect to falls prevention, and highlight the need for more nuanced work to better ascertain the association between home hazards and falls for those with and without visual impairments.

The effect of lighting on fall rates was considerable in our cohort, with each 10-fold lux increase in room lighting lowering the rate of falls by roughly 35%. While such differences in lighting seem large, they are frequently encountered in normal daily life. For example, outdoor lighting in full daylight is typically 10-fold higher than outdoor lighting on an overcast day, which is 100-fold higher than outdoor lighting at twilight.43 While poor lighting has been evaluated as a specific hazard in prior home assessment tools, and has been noted as a common factor predisposing older adults to falls, very few studies have tried to link in-home lighting to fall rates.44 To more correctly link lighting to the risk of falls, we evaluated lighting within the various regions of the home and modeled fall rates within each specific region of the home to the level of the lighting within that particular home region, as described in Figure 1. We hypothesized that poor lighting would be of particular danger in persons with worse glaucoma damage as glaucoma patients have impaired contrast,45 and persons with contrast impairment have been observed to need better lighting in navigation and recognition of objects, even over a range of lighting where performance would have plateaued for a normally-sighted person.46 However, our results did not appear to indicate an interaction between integrated visual field sensitivity, highly correlated with contrast sensitivity in this population,45 and lighting with regards to fall rates, suggesting that poor lighting is equally a problem for all older adults, at least at the levels of lighting typically encountered in their homes. While these findings raise the possibility that improved lighting may reduce falls, further study is needed to establish this connection, and indeed, pure lighting interventions are not well-studied as a fall reduction technique.

Our data suggest that better lighting in the home is associated with fewer falls, although our study was not designed to determine the optimal amount of lighting needed to minimize falls. We did note, however, that home regions with lighting levels of 30 footcandles (323 lux) had lower fall rates than home regions below this level of lighting, though similar findings would also have been attained for a number of other lighting thresholds (data not shown). Presumably, beyond some level, additional lighting would have limited benefits,46 or might even be harmful, though the typical lighting levels we observed were below those recommended as part of design standards and guidelines.47,48 Moreover, better lighting levels will likely be accompanied by higher energy expenditures. While lighting efficient bulbs which produce similar lighting levels and/or last longer have been generated,49 such bulbs come at higher up-front costs which may not be affordable for all.

While lighting, analyzed as a specific hazard, posed a higher risk of falls, neither the overall number of hazards, nor the percentage of potentially hazardous items graded as hazardous was associated with a higher risk of falls. Indeed, while numerous home assessment tools designed to judge the risk of falls in the home have been created and internally validated10,32,50–55 only a handful have been externally validated – tested to see if they can predict home fall rates.18,19,56,57 Those that have attempted external validation have reported mixed results, with only some studies noting a higher rate of falls in homes with more hazards. However, very few of these studies linked location of the hazard with location of the fall,18,56 as was done here. One would not expect a hazard to be linked to a fall except when the hazard was found in the same region of the fall. Despite linking fall and hazard locations, we did not find any association between falls and home hazards. Of note, while we were able to link the hazards and falls based on location, we did not weight hazards (some may produce falls much more than others), nor did we account for the amount of activity that occurs in the vicinity of the hazards (patients may expose themselves to some hazards much more than others). These shortcomings may explain the discrepancy between attempts to externally validate home assessment tools against fall rates and studies which have found that home modification can lower fall rates (though not all such studies have been successful). Going forwards, it is likely that a more nuanced approach to evaluating hazards is needed, in which the amount of activity (i.e. steps) taking place in the vicinity of the hazard is considered, as is the severity of the hazard (i.e. not weighting all hazards equally).

We previously noted that the amount of lighting varies across regions of the home,31 with the lowest lighting (median values of 100 lux or less) observed in the stairs, bedroom, hallway and entryway. Two of these regions (stairs and bedroom) were noted to have the highest risk of falling, suggesting that poor lighting may contribute to falls in these areas, and these 2 areas (along with hallways) were found to be the three most common regions of the home not meeting recommended levels of lighting. However, room-specific fall rates depend not only on the inherent risk of falling in the room, but also the amount of activity done in the room. Thus, while our results were able to identify the areas of the home with the highest risk of falling over a period of time (stairs, bedroom), they were not able to distinguish the areas of the home where walking is most dangerous. Indeed, the fall risk per step taken in each region of the home may be quite different than the rate of falls per time in the same region. The stairs, for example, may not be a frequent area of walking, and the high number of falls combined with the relative paucity of steps taken in this region, may indicate that activity on steps may be significantly more dangerous. Further experiments in which both the falls and steps in each region of the home are quantified are required to obtain a complete picture regarding the most dangerous regions of the home.

Limitations of the study include that we were not able to identify the number of steps taken in each region of the home, which would have allowed us to link hazards more directly to falls that may have resulted from that hazard, altering our conclusions. Also, while lighting was measured as part of a direct home assessment visit, the lighting at the precise time of the falls is unknown, and may have differed from what we measured. Furthermore, it is possible that participants may have changed their homes (lighting and hazard) after their baseline home assessment, though in a follow-up questionnaire one year into the study, very few reported (23 [12%]) doing so. Finally, while home regions with worse lighting were noted to have more falls, it is possible these regions of the home were more dangerous for other reasons besides lighting, i.e. inherent features of the room such as changes in elevation or the type of activity done in the room.

In conclusion, the findings from this study demonstrate that better room lighting is associated with fewer falls in the homes of individuals with a range of visual impairment. No association was found between home hazards and fall rates, although given the effectiveness of home modification efforts in reducing fall rates in some studies, further work is required which considers the severity of hazards and the degree to which individuals are exposed to hazards (i.e. by measuring the number of steps taken in the vicinity of the hazard). Further work should explore if environmental modifications can minimize fall rates, as they provide a simple, relatively low-cost method for addressing falls that requires limited input from the individual, and can be standardized and disseminated through building and architectural codes.

Figure 5.

Figure 5.

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Modeled within-room annual rate of falls by level of room lighting.

ACKNOWLEDGMENTS

Project funding was provided by National Institutes of Health Grant EY002976 and Research to Prevent Blindness. No other relevant financial disclosures were noted for any authors. All authors contributed substantially to the manuscript including design and conduct of the study (PYR, AM, RBM, SKW, LNG, DSF), data collection (AM, RBM), data management (AM, RBM), data analysis (PR, AM, JE), data interpretation (PYR, AM, JE), manuscript preparation (PYR, AM), and critical review of the manuscript (PYR, AM, JE, SKW, LNG, DSF).

Footnotes

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REFERENCES

  • 1.Burns ER, Stevens JA, Lee R. The direct costs of fatal and non-fatal falls among older adults — United States. J Safety Res 2016;58:99–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Scheffer AC, Schuurmans MJ, van Dijk N, et al. Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing 2008;37:19–24. [DOI] [PubMed] [Google Scholar]
  • 3.Berry SD, Miller RR. Falls: Epidemiology, pathophysiology, and relationship to fracture. Curr Osteoporos Rep 2008;6:149–154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Anon. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Web–based Injury Statistics Query and Reporting System (WISQARS) [online]. [Google Scholar]
  • 5.Filer W, Harris M. Falls and Traumatic Brain Injury Among Older Adults: TABLE 1. N C Med J 2015;76:111–114. [DOI] [PubMed] [Google Scholar]
  • 6.Tinetti ME, Williams CS. Falls, Injuries Due to Falls, and the Risk of Admission to a Nursing Home. New Engl J Medicine 1997;337:1279–1284. [DOI] [PubMed] [Google Scholar]
  • 7.Sotimehin AE, Yonge AV, Mihailovic A, et al. Locations, Circumstances, and Outcomes of Falls in Patients With Glaucoma. Am J Ophthalmol 2018;192:131–141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Stevens JA, Mahoney JE, Ehrenreich H. Circumstances and outcomes of falls among high risk community-dwelling older adults. Inj Epidemiology 2014;1:5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Campbell A, Borrie M, Spears G, et al. Circumstances and consequences of falls experienced by a community population 70 years and over during a prospective study. Age Ageing 1990;19:136–41. [DOI] [PubMed] [Google Scholar]
  • 10.Tomita MR, Saharan S, Rajendran S, et al. Psychometrics of the Home Safety Self-Assessment Tool (HSSAT) to Prevent Falls in Community-Dwelling Older Adults. Am J Occup Ther 2014;68:711. [DOI] [PubMed] [Google Scholar]
  • 11.Clemson L, Fitzgerald MH, Heard R. Content Validity of an Assessment Tool to Identify Home Fall Hazards: The Westmead Home Safety Assessment. Br J Occup Ther 1999;62:171–179. [Google Scholar]
  • 12.Letts L, Scott S, Burtney J, et al. The Reliability and Validity of the Safety Assessment of Function and the Environment for Rehabilitation (SAFER Tool). Br J Occup Ther 1998;61:127–132. [Google Scholar]
  • 13.Mackenzie L, Byles J, Higginbotham N. Reliability of the Home Falls and Accidents Screening Tool (HOME FAST) for identifying older people at increased risk of falls. Disabil Rehabil 2009;24:266–274. [DOI] [PubMed] [Google Scholar]
  • 14.Gitlin L, Schinfeld S, Winter L, et al. Evaluating home environments of persons with dementia: interrater reliability and validity of the Home Environmental Assessment Protocol (HEAP). Disabil Rehabil 2009;24:59–71. [DOI] [PubMed] [Google Scholar]
  • 15.Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society. Summary of the Updated American Geriatrics Society/British Geriatrics Society Clinical Practice Guideline for Prevention of Falls in Older Persons. J Am Geriatr Soc 2011;59:148–157. [DOI] [PubMed] [Google Scholar]
  • 16.Anon. CDC National Center for Injury Prevention and Control. Preventing Falls: A Guide to Implementing Effective Community-Based Fall Prevention Programs. [Google Scholar]
  • 17.Lord SR, Menz HB, Sherrington C. Home environment risk factors for falls in older people and the efficacy of home modifications. Age Ageing 2006;35:ii55–ii59. [DOI] [PubMed] [Google Scholar]
  • 18.Gill TM, Williams CS, Tinetti ME. Environmental Hazards and the Risk of Nonsyncopal Falls in the Homes of Community-Living Older Persons. Med Care 2000;38:1174–1183. [DOI] [PubMed] [Google Scholar]
  • 19.Letts L, Moreland J, Richardson J, et al. The physical environment as a fall risk factor in older adults: Systematic review and meta-analysis of cross-sectional and cohort studies. Aust Occup Ther J 2010;57:51–64. [DOI] [PubMed] [Google Scholar]
  • 20.Black AA, Wood JM, Lovie-Kitchin JE. Inferior Field Loss Increases Rate of Falls in Older Adults with Glaucoma. Optometry & Vision Science 2011;88:1275. [DOI] [PubMed] [Google Scholar]
  • 21.Coleman AL, Cummings SR, Ensrud KE, et al. Visual Field Loss and Risk of Fractures in Older Women. J Am Geriatr Soc 2009;57:1825–1832. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Coleman AL, Cummings SR, Yu F, et al. Binocular Visual-Field Loss Increases the Risk of Future Falls in Older White Women. Journal of the American Geriatrics Society 2007;55:357–364. [DOI] [PubMed] [Google Scholar]
  • 23.Freeman EE, Muñoz B, Rubin G, West SK. Visual Field Loss Increases the Risk of Falls in Older Adults: The Salisbury Eye Evaluation. Investigative Ophthalmology & Visual Science 2007;48:4445–4450. [DOI] [PubMed] [Google Scholar]
  • 24.BOptom RQ, Cumming RG, Mitchell P, Attebo K. Visual Impairment and Falls in Older Adults: The Blue Mountains Eye Study. Journal of the American Geriatrics Society 1998;46:58–64. [DOI] [PubMed] [Google Scholar]
  • 25.Pighills A, Ballinger C, Pickering R, Chari S. A critical review of the effectiveness of environmental assessment and modification in the prevention of falls amongst community dwelling older people. Brit J Occup Ther 2015;79:133–143. [Google Scholar]
  • 26.Kamei T, Kajii F, Yamamoto Y, et al. Effectiveness of a home hazard modification program for reducing falls in urban community-dwelling older adults: A randomized controlled trial. Jpn J Nurs Sci 2015;12:184–197. [DOI] [PubMed] [Google Scholar]
  • 27.Gillespie LD, Robertson CM, Gillespie WJ, et al. Interventions for preventing falls in older people living in the community. Cochrane Db Syst Rev 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Chase C, Mann K, Wasek S, Arbesman M. Systematic Review of the Effect of Home Modification and Fall Prevention Programs on Falls and the Performance of Community-Dwelling Older Adults. Am J Occup Ther 2012;66:284–291. [DOI] [PubMed] [Google Scholar]
  • 29.Kasneci E, Black AA, Wood JM. Eye-Tracking as a Tool to Evaluate Functional Ability in Everyday Tasks in Glaucoma. Journal of Ophthalmology 2017;2017:1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Lee S, Black AA, Wood JM. Effect of glaucoma on eye movement patterns and laboratory-based hazard detection ability. Plos One 2017;12:e0178876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Yonge AV, Swenor BK, Miller R, et al. Quantifying Fall-Related Hazards in the Homes of Persons with Glaucoma. Ophthalmology 2017;124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Swenor BK, Yonge AV, Goldhammer V, et al. Evaluation of the Home Environment Assessment for the Visually Impaired (HEAVI): an instrument designed to quantify fall-related hazards in the visually impaired. BMC Geriatrics 2016;16:214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Mihailovic A, Swenor BK, Friedman DS, et al. Gait Implications of Visual Field Damage from GlaucomaMihailovic et al. Translational Vision Science & Technology 2017;6:23–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Chang TC, Ramulu P, Hodapp E. Clinical decisions in glaucoma, 2nd edition. 2016.
  • 35.Davalos-Bichara M, Lin FR, Carey JP, et al. Development and Validation of a Falls-Grading Scale. Journal of Geriatric Physical Therapy 2013;36:63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Ramulu PY, Mihailovic A, West SK, et al. Predictors of falls per step and falls per year at and away from home in glaucoma. Am J Ophthalmol 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Ramulu PY, Chan ES, Loyd TL, et al. Comparison of home and away-from-home physical activity using accelerometers and cellular network-based tracking devices. Journal of physical activity & health 2011;9:809–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Kim S-Y, Yun J. Determining daily physical activity levels of youth with developmental disabilities: days of monitoring required? Adapted physical activity quarterly : APAQ 2009;26 220–35. [DOI] [PubMed] [Google Scholar]
  • 39.Hart TL, artz A, Cashin SE, Strath SJ. How many days of monitoring predict physical activity and sedentary behaviour in older adults? International Journal of Behavioral Nutrition and Physical Activity 2011;8:62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.TOGO F, WATANABE E, PARK H, et al. How Many Days of Pedometer Use Predict the Annual Activity of the Elderly Reliably? Medicine & Science in Sports & Exercise 2008;40:1058. [DOI] [PubMed] [Google Scholar]
  • 41.Hochberg C, Maul E, Chan ES, et al. Association of Vision Loss in Glaucoma and Age-Related Macular Degeneration with IADL DisabilityIADL Disability in Glaucoma and AMD. Investigative Ophthalmology & Visual Science 2012;53:3201–3206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Gnjidic D, Hilmer SN, Blyth FM, et al. Polypharmacy cutoff and outcomes: five or more medicines were used to identify community-dwelling older men at risk of different adverse outcomes. Journal of Clinical Epidemiology 2012;65:989–995. [DOI] [PubMed] [Google Scholar]
  • 43.Engineering Toolbox (online). Available at: https://www.engineeringtoolbox.com/light-level-rooms-d_708.html#:~:text=Earlier%20it%20was%20common%20with,even%20approach%201500%20%2D%202000%20lux. Accessed December 29th, 2020.
  • 44.Bakker R, Iofel Y, Lachs MS. Lighting Levels in the Dwellings of Homebound Older Adults. J Hous Elder 2004;18:17–27. [Google Scholar]
  • 45.Lin S, Mihailovic A, West SK, et al. Predicting Visual Disability in Glaucoma With Combinations of Vision Measures. Translational Vision Science & Technology 2018;7:22–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Cornelissen FW, Bootsma A, Kooijman AC. Object perception by visually impaired people at different light levels. Vision Res 1995;35:161–168. [DOI] [PubMed] [Google Scholar]
  • 47.Electrical Engineering portal. Guidelines To Residential Room Lighting (online). Available at: https://electrical-engineering-portal.com/guidelines-to-residential-room-lighting. Accessed December 29th, 2020.
  • 48.Lighting for the Aged and Partially Sighted Committee. (1998). Lighting and the Visual Environment for Senior Living (Publication No. RP-28–98). New York: The Illuminating Engineering Society of North America. [Google Scholar]
  • 49.Energy Saving Trust. The right light: Selecting low energy lighting. Introduction for designers and house builders. April 2016 (online). Available at: https://energysavingtrust.org.uk/sites/default/files/reports/EST%20Lighting%20Guide%20-%20the%20Right%20Light.pdf. Accessed December 29th, 2020.
  • 50.Tan P, Khoo E, Chinna K, et al. Individually-tailored multifactorial intervention to reduce falls in the Malaysian Falls Assessment and Intervention Trial (MyFAIT): A randomized controlled trial. Plos One 2018;13:e0199219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Walker B, Hoke K, Manley M, et al. Establishing the Reliability and Validity of Health in Motion© Automated Falls Screening Tool. Adv Aging Res 2018;07:39–51. [Google Scholar]
  • 52.Aplin T, Ainsworth E. Clinical utility of the In-Home Occupational Performance Evaluation (I-HOPE) for major home modification practice in Australia. Aust Occup Ther J 2018;65:431–438. [DOI] [PubMed] [Google Scholar]
  • 53.Romli M, Mackenzie L, Lovarini M, et al. The interrater and test-retest reliability of the Home Falls and Accidents Screening Tool (HOME FAST) in Malaysia: Using raters with a range of professional backgrounds. J Eval Clin Pract 2017;23:662–669. [DOI] [PubMed] [Google Scholar]
  • 54.Mackenzie L, Byles J. Scoring the home falls and accidents screening tool for health professionals (HOME FAST-HP): Evidence from one epidemiological study. Aust Occup Ther J 2018;65:346–353. [DOI] [PubMed] [Google Scholar]
  • 55.Horowitz BP, Nochajski SM, Schweitzer JA. Occupational therapy community practice and home assessments: use of the home safety self-assessment tool (HSSAT) to support aging in place. Occup Ther Heal Care 2013;27:216–27. [DOI] [PubMed] [Google Scholar]
  • 56.Leclerc B-S, Bégin C, Cadieux É, et al. Relationship between home hazards and falling among community-dwelling seniors using home-care services. Revue D’épidémiologie Et De Santé Pu lique 2010;58 3–11. [DOI] [PubMed] [Google Scholar]
  • 57.Huang T-T. Home Environmental Hazards Among Community-Dwelling Elderly in Taiwan. J Nurs Res 2005;13:49. [PubMed] [Google Scholar]

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