Association of balance, anthropometric measurements, fall risk, and awareness with quality of life in older females according to hypertensive status

Abstract

Falls and their consequences are perhaps the greatest moderators of quality of life (QoL) among older adults with hypertension. However, limited studies have been conducted to identify associations between fall risk and awareness, anthropometric, balance, and QoL among older women with different blood pressure patterns within the Pakistani population. This cross-sectional study examined the relationship between anthropometric measurements, postural balance (PB), fear of falling, fall risk awareness, and QoL in older females with hypersensitive status in Lahore, Pakistan. A total of 114 females aged 65 to 83 years from Bajwa hospital participated in the study. Anthropometric measurements, including height, weight, body mass index (BMI), and waist-to-height ratio, were calculated. Time up and go test, Berg balance scale (BBS), fall efficacy scale international (FES-I), and fall risk awareness questionnaire were used. QoL was measured through SF-36. Independent t-tests, Pearson correlation, and multiple regression analysis were conducted. Comparison of hypertensive and normotensive participants showed statistically significant differences in BMI (P = .006), waist-to-height ratio (P < .001), and balance measured by BBS (P = .013). Compromised mobility was observed among hypertensive and obese women. Multiple regression models showed that the domains of emotional well-being and role limitations due to physical health could be predicted by PB, fear of falling, and fall risk awareness. These results revealed obesity and fear of falling as key factors to predict QoL. The waist-to-height ratio (an indicator of central obesity) was associated more closely with poor PB in comparison to the BMI (R = 0.75 vs R = 0.36), and the fear of falling positively correlated with poor acute awareness of fall risk (R = 0.28, P < .05). The variables related to QoL differ based on hypertensive status. Among elderly hypertensive females, obesity, reduced balance, and fear of fall represent the key determinants of decreased emotional well-being and role limitation due to physical health. Strategies to prevent falls need to be individualized and multifactorial.

1. Introduction

Falls are a significant source of elderly public-health-related issues in older adults around the globe; world prevalence rates are calculated between 18% and 33%, and fall incidents are related to injuries, disability, and a reduced quality of life (QoL).^[1] The fall-injury rate in Pakistan is also reported to be 8.9 cases per 1000 persons per year by the National injury Survey.^[2] Fall etiology is multi-factorial and includes postural instability, physiological aging, visual impairments, medication use, and chronic medical conditions, especially high blood pressure.^[3,4] The prevalence of falls in older adults with hypertension is over 32.2%, which means that they are particularly exposed to the issue.^[5]

Hypertension is one of the most common non-communicable conditions in older adults and is closely linked to mobility loss, higher risk of falls, and lower QoL.^[6] Medication therapy, also known as pharmacological treatment, is often used.^[7,8] This type of treatment involves medications that can increase the risk of falls due to hypotension, dizziness, or bradycardia, such as diuretics or beta-blockers. The overall effect of these treatments on falls has been debated in the literature.^[9,10]

At the same time, obesity and changes in body composition contribute to the development of postural instability and an increased risk of falls in older adults. Higher body mass index (BMI) and waist-to-height ratio (WHtR) cause a forward shift of the center of mass, which negatively affects gait and balance.^[11] All these biomechanical changes, along with weakened muscle strength and reduced joint mobility, increase susceptibility to instability. Musculoskeletal disorders such as osteoarthritis and sarcopenic obesity further exacerbate postural and mobility problems in obese older adults.^[12]

Besides the obvious physical risks attributed to falls, psychological factors, in particular the fear of falling (FoF) and fall risk awareness, are key factors in the health of the older adult population.^[13] To provide empirical evidence, FoF is highly correlated with activity restriction, reduced independence, and social isolation, all of which may affect the QoL.^[6] In comparison, it has been noted that fall risk awareness influences preventive practices and self-care, which are likely to reduce falls in the future.^[14]

QoL is a complex measurement involving multiple aspects in terms of physical, emotional, and social. QoL of older adults is also regulated not only by chronic conditions like hypertension and obesity but also by mobility, functional independence, and psychological security.^[15] Past research indicated that falls, together with the factors that accompany them, have a significant negative effect on QoL, especially in emotional and physical role limitations and social participation.^[16]

Due to the combined effects of age-related physiological changes, cardiovascular impairments, and musculoskeletal decline that contribute to falls, older hypertensive women are at a greater risk of postural balance (PB) issues, falls, and fall-related complications.^[3] Additionally, fear of falling, whether or not a fall has happened, can lead to activity restrictions, loss of confidence, social isolation, and psychological distress, all of which decrease QoL.^[17] Furthermore, an individual’s behavior and participation in preventive actions are heavily influenced by their understanding of their fall risk. Conversely, being overly cautious or a lack of awareness can be equally harmful, possibly increasing exposure to risky situations or causing unnecessary dependence.^[18] Although extensive research has explored the relationships between anthropometric indices, PB, fall risk, and QoL in aging women globally, there is a lack of local data from Pakistan analyzing these connections among older women, especially considering hypertensive status. The current study aims to address this gap by examining the relationship between these factors and QoL among hypertensive and normotensive older women in Lahore, Pakistan.

2. Materials and methods

2.1. Study design

A cross-sectional study design was used to conduct the current research from March to June 2024. This design was selected because it has the advantage of being useful in collecting data in a short time from a diverse population, as well as making it possible to determine relations/associations between variables in a single phase.^[19] The data was collected from Bajwa hospital, Lahore. The study was approved by the research ethics review committee for Research involving human research participants, Bajwa hospital and cardiac center (Ref. No. BH/PTD/049) and Chulalongkorn University (COA: 129/67) before data collection.

2.2. Study participants

The estimation of sample size was conducted through G Power software, with the premise of a fixed-effects linear multiple regression model (alpha = 0.05, power = 0.80). The minimum of the sample was 114. The participants included elderly women (aged 65 or more) who received routine care and were still able to perform their self-care activities independently. A convenience non-probability design was used because probability-based sampling procedures were not feasible in the clinical setting.

Cardiologists or specialized doctors diagnosed hypertension through the health records of the patients or current pharmacologic management. The individuals were not included in the research if they had a documented history of neurological diseases (e.g., stroke, multiple sclerosis), had limb deformities, painful surgeries of the limbs in the last 6 months, dizziness/tinnitus, or had taken sedative or hypnotic drugs. Cognitive status was determined by the administration of the mini-mental state examination; only the participants with normal scores were admitted. Written informed consent was obtained from all the participants before data collection.

2.3. Study variables

2.3.1. Anthropometric measurements

Patients’ weight and height were recorded. BMI and waist-to-height ratio were calculated afterward.

2.3.2. Time up and go test (TUG)

The TUG test is one of the most popular and easy-to-use comprehensive assessment tools for testing functional performance and mobility in aged people. The assessment begins with the subject in a chair with side arms. When the subjects/participants are told to go, they stand up from the chair, walk at a comfortable pace for 3 meters (10 feet), move back to the chair, and sit down. The time that is associated with the task is recorded in seconds. It is a reliable and valid tool.^[20]

2.3.3. Berg balance scale (BBS)

The BBS is used to determine a person’s balance and fall risk. Numerous static and dynamic activities, such as standing with eyes closed, standing on one leg, reaching forward, turning, and changing postures, are included in the BBS assignments. A 5-point ordinal scale, ranging from 0 to 4, is used to score each work according to the performer’s ability to do it without falling or needing help from others. It has high reliability and internal consistency.^[21]

2.3.4. Fall efficacy scale international (FES-I)

It is used to gauge a person’s confidence or fear of falling in different situations. The 16 items that make up the FES-I encompass a range of everyday activities and situations that might be linked to an increased risk of falling. Participants assess how worried they are about falling throughout each activity on a 4-point Likert scale, with “not at all concerned” to “very concerned.” It is also a valid and reliable tool.^[22]

2.3.5. 36 items short form survey (SF-36)

SF-36 presents a comprehensive picture of an individual’s physical, mental, and social well-being by assessing their perception of their health in several different aspects. Each domain’s scores on the SF-36 items are converted into a range of 0 to 100. Higher scores show a better QoL. It is with 85% reliability and good validity.^[23]

2.3.6. Fall risk awareness questionnaire (FRAQ)

The falls risk assessment questionnaire (FRAQ) has 22 components that evaluate an individual’s knowledge of medical, environmental, pharmacologic, and physical risk factors for falls, in addition to their demographics, medical history, and fall history. Each response receives a score of 1. It is a valid tool and can play an important role in educating patients.^[24]

2.4. Data collection procedure

After the institutional approval, eligible participants were selected by approaching the outpatient departments of Bajwa hospital. The data about demographics and anthropometry measures were collected after informed consent. Waist measures, height, and weight were measured in standard ways. To perform balance/mobility tests (TUG and BBS), the participants were requested to take off their footwear. Test areas were maintained clean and safe, and a first aid kit was kept during the testing.

The entire physical performance tests and questionnaire administration were carried out by the principal researcher, who was trained in performing all the assessments. Since it was an observational study, blinding was not implemented. To decrease bias and enhance internal validity, all evaluations were administered using standardized administration guidelines. Participants were subsequently asked to fill out a FES-I, a FRAQ, and an SF-36 questionnaire. Questionnaires were read aloud to illiterate participants or to those who had visual issues; a response was written down with the help of a caregiver or the researcher.

2.5. Data analysis

All analysis was conducted using SPSS version 27, with the significance level set at P < .05. Descriptive statistics were used to summarize the demographic characteristics of the study participants. Mean, standard deviation, minimum, and maximum values were calculated for age, height, weight, BMI, waist-to-height ratio, and scores of FES-I and FRAQ. Independent sample t-tests were used to determine differences between hypertensive and normotensive participants. Frequency was calculated for BBS categories (scores between 0 and 20 meant high fall risk, 21 and 40 meant medium fall risk, and 41 and 56 meant low fall risk). The score for each of the domains of SF-36 was recorded as a percentage, i.e., 0 to100, with a higher percentage indicating better QoL. Multiple regression analysis was used to assess the effect of anthropometric measurements, fear of falling, balance, and fall awareness, on QoL.

3. Results

The current study involved 114 community-dwelling elderly women. Among them, 78 (68.4%) were hypertensive, and 36 (31.6%) were normotensive. The participants had a mean age of 71.7 ± 5.0 years and a height of 1.6 ± 0.1 meters. Hypertensive women had a mean BMI of 29.8 kg/m² ± 6.6, and normotensive women had a mean BMI of 27.3 kg/m² ± 5.1, with respective waist-to-height ratios of 47.9 kg/m⁻¹ ± 8.9 and 39.5 kg/m⁻¹ ± 6.4. The significant differences between the groups were found in weight, BMI, waist circumference, and waist-to-height ratio, as hypertensive women featured higher anthropometric parameters (see Table 1).

Table 1.

Participants’ characteristics.

Sr. No.	Demographics	Hypertensive	Normotensive	P-value
1.	Age in years	73.1 ± 5.8	70.3 ± 4.6	.195
2.	Height	1.70 ± 0.08	1.72 ± 0.10	.230
3.	Weight	69.2 ± 7.1	61.1 ± 7.7	.005**
4.	BMI	30.5 ± 6.7	27.3 ± 5.1	.006**
5.	Waist circumference	87.1 ± 6.9	79.9 ± 6.7	.005**
6.	Hip circumference	35.6 ± 2.6	33.7 ± 2.5	.049*
7.	Waist-to-height ratio	47.9 ± 8.9	39.5 ± 6.4	.000**
8.	Mobility assessment by TUG	9.66 ± 3.20	9.22 ± 3.66	.725
9.	BBS	0.71 ± 0.47	1.22 ± 0.55	.013*
10.	Fear of fall (FES-I)	40.9 ± 10.8	46.6 ± 11.1	.155
11.	Fall awareness (FRAQ)	36.5 ± 6.0	35.7 ± 7.1	.728
12.	Quality of life (SF-36)
	Physical functioning	39.3 ± 25.4	41.7 ± 22.7	.637
	Role limitation due to physical health	47.6 ± 25.2	35.2 ± 31.3	.826
	Role limitation due to emotional problem	49.3 ± 14.9	46.8 ± 12.2	.204
	Energy or fatigue	49.7 ± 15.6	58.4 ± 14.3	.602
	Emotional well-being	62.5 ± 17.0	65.3 ± 19.9	.109
	Social functioning	56.1 ± 28.0	60.7 ± 15.9	.652
	Pain	48.0 ± 14.0	58.9 ± 14.8	.559
	General health	63.1 ± 5.8	70.3 ± 4.6	.043*

BBS = Berg balance scale, FES-I = fall efficacy scale international, FRAQ = fall risk awareness questionnaire, SF-36 = 36 items short form survey, TUG = time up and go test.

^*P < .05 significant.

^**P < .01 significant.

3.1. Analysis of TUG performance by hypertension status and BMI

TUG was categorized into normal mobility (10 seconds), good mobility (11–20 seconds), and problematic mobility (>20 seconds).^[20] The comparison of TUG performance among hypertensive and normotensive older adults showed that a relatively high percentage of normotensive participants had normal mobility. The problematic mobility was only found among hypertensive participants. Similarly, the cross-tabulation between BMI and TUG showed that most participants who had a normal BMI fell mainly in normal mobility. Notably, the obese group had all the problematic mobility, and none of the normal BMI group fell in the problematic mobility category (see Tables 2 and 3).

Table 2.

Cross-tabulation between TUG and hypertension status.

TUG performance	Hypertensive	Normotensive	Total
Normal mobility	29	24	53
Good mobility	45	11	56
Problematic mobility	4	1	5
Total	78	36	114

TUG = time up and go test.

Table 3.

Cross-tabulation between TUG and BMI.

TUG performance	Normal	Overweight	Obese	Total
Normal mobility	13	10	30	53
Good mobility	5	4	47	56
Problematic mobility	0	0	5	5
Total	18	14	82	114

BMI = body mass index, TUG = time up and go test.

3.2. Pearson correlation between variables

An independent Pearson correlation of the variables of the study revealed a number of statistically significant relations (see Table 4). A moderate positive correlation of BMI was found with the waist-to-height ratio (WHtR). BMI and WHtR were found to have a positive correlation with BBS and the fall efficacy scale international (FES-I) indicators, with higher anthropometric values correlating with higher balance impairment indicators and fear of falling. The strongest correlation was seen between WHtR and BBS, which indicates that central obesity has a much better connection with PB restriction than BMI alone. BBS and FES-I were negatively correlated as per expectations, where a high score in the balance had a positive correlation with a low fear of falling. It is worth noting that there was an inverse correlation between FES-I and the fall risk awareness questionnaire (FRAQ), implying that the higher awareness about the risks of falls, the less the fear of falling. No significant relationships were observed between FRAQ and anthropometric measurement or balance.

Table 4.

Pearson correlation between all outcome measures.

Variables	BMI	WHtR	BBS	FES-I	FRAQ
BMI	1
WHtR	.38*	1
BBS	.36*	.75**	1
FES-I	.25*	.44*	.65**	1
FRAQ	.05	.01	−.08	−.28*	1

BBS = Berg balance scale, BMI = body mass index, FES-I = fall efficacy scale international, FRAQ = fall risk awareness questionnaire, WHtR = waist-to-height ratio.

^*P < .05 significant.

^**P < .01 significant.

3.3. Multiple regression analysis

To identify the predictors of the QoL domains among hypertensives, normotensives, and for all cases, multiple regressions were applied (see Table 5). Within the hypertensive group, the emotional well-being domain of the SF-36 was statistically significant, F(4, 73) = 3.005, P = .024, which explained 14.1% of the variance. BBS and FES-I were found to be important predictors, meaning that the improved postural control and low fear of falls were linked to improved emotional well-being. Similarly, in normotensive participants, the regression model was significant for the emotional well-being domain, F(4, 31) = 2.831, P = .041, and it explained 26.8% variation (R² = .268). BBS, FES-I, and TUG significantly predicted this domain.

Table 5.

Multiple regression analysis of significant models of SF-36 domains.

Independent variables	β	t	P-value	95% CI [lower limit, upper limit]
Model 1: regression analysis of emotional well-being (domain of SF-36) for hypertensive females
FRAQ	.331	1.558	.124	[−.092, .755]
BBS	6.160	2.102	.039*	[.321, 11.999]
FES-I	−.388	−2.004	.049*	[−.775, −.002]
TUG	.055	.154	.878	[−.657, .767]
Model 2: regression analysis of emotional well-being (domain of SF-36) for normotensive females
FRAQ	.535	1.875	.070	[−.047, 1.116]
BBS	10.878	2.430	.021*	[1.748, 20.008]
FES-I	−.676	−2.087	.045*	[−1.336, −.015]
TUG	−1.365	−2.271	.030*	[−2.590, −.139]
Model 3(a): regression analysis of role limitation due to the physical health domain (domain of SF-36) for all cases
FRAQ	−.782	−2.518	.013*	[−1.398, −.167]
BBS	11.541	2.602	.011*	[2.751, 20.330]
FES-I	−.558	−2.146	.034*	[−1.073, −.043]
TUG	−.736	−1.332	.186	[−1.83, 2.360]
Model 3(b): regression analysis of emotional well-being (domain of SF-36) for all cases
FRAQ	.380	2.230	.028*	[.042, .717]
BBS	6.767	2.786	.006**	[1.953, 11.582]
FES-I	−.355	−2.493	.014*	[−.637, −.073]
TUG	−.269	−.887	.377	[−.869, .332]

BBS = Berg balance scale, FES-I = fall efficacy scale international, FRAQ = fall risk awareness questionnaire, SF-36 = 36 items short form survey, TUG = time up and go test.

^*P < .05 significant.

^**P < .01 significant.

When all cases were included, the regression model was significant for the role limitation due to physical health domain, F(4, 109) = 3.423, P = .011, and emotional well-being, F(4, 109) = 4.287, P = .003. The models explained 11.2% and 13.6% of the variance, respectively. As in other models, balance, fear of falling, and fall risk awareness were major predictors.

4. Discussion

The present study aimed to determine the impact of PB, anthropometric measurements, fear of falling (fof), and fall risk awareness on the QoL among hypertensive and normotensive elderly women in Pakistan. The results showed significant associations between anthropometric measures and fall risk in elderly females. The findings of the current research support existing literature and enhance our understanding of the specific impact of hypertension status on balance, fear of falling, and emotional well-being. Hypertensive individuals had significantly greater anthropometric scores and especially BMI and waist-to-height ratio, when compared with normotensives. These findings are in line with the preexisting literature that shows that there is a significant relationship between high blood pressure, central obesity, and low physical functioning in elderly individuals.^[25,26] The current paper advances such evidence by demonstrating that postural stability and emotional well-being become diminished to a higher degree among hypertensive patients with such physical indicators as well.

Cross-tabulation showed that difficulty in mobility (as measured by the timed up and go test) was limited to those who are hypertensive, obese women, and none of those with normal BMIs. Such a pattern stresses the biomechanical and physiological demands of overweight and cardiovascular impairment to facility-based activities that incorporate mobility.^[27] It is therefore imperative that multifaceted interventions that encompass both cardiovascular management and elements of weight control should be implemented to reduce fall risk among this group of people.

Psychological factors were also proven to be significant predictors of QoL. In hypertensive, normotensive, and pooled populations, fear of falling (FES-I) remained a significant independent negative factor in the emotional well-being domain, whereas balance performance (evaluated with the BBS) was an effective positive factor in both emotional well-being and role limitations due to physical health. These findings support the studies that found fear of falling encourages activity restrictions, socialization, and reduced psychological resilience, and they also highlight the importance of functional mobility and confidence in maintaining adequate QoL.^[16,28,29]

The findings of the current research demonstrate the different effects of mobility factors and fall risk perceptions on the health-related QoL of older adults. Despite being a statistically significant predictor of QoL in the normotensive participants, TUG performance failed as a predictor in other regression models. It can be suggested that, despite the fact that gait speed and agility are considered to be universal measures of physical ability, their response-specific explanatory power might be secondary to that of the psychological and postural control perspectives represented by the FES-I and BBS.^[30] Such observation is aligned with what has been previously mentioned in the literature, that subjective perception of fall risk and balance confidence have a more significant impact on psychosocial outcomes compared to raw mobility speed on its own.^[31]

The predictive quality of fall risk awareness (FRAQ) was inconsistent. FRAQ was a major predictor of all combined models and physical role limitations subgroup analyses, but it is not statistically significant in many of the individual models of subgroups. The negative correlation of FRAQ with fear of falling further supports the psychological benefits of fall risk awareness, with its implication in fall-prevention education.^[32]

The regression models explained a relatively small, but statistically significant, proportion of variance in QoL outcomes (11.2%–26.8%). The findings are a demonstration of the fact that there is a multidimensionality of relationship where the 3 domains, physical, functional, and psychological, meet to shape the QoL in older groups of people. Notably, the different trends of predictive models between hypertensive and normotensive subgroups are significant since they suggest that strategies to prevent falls and rehabilitation in older adults should be personalized based on their clinical profiles.

4.1. Study limitations and recommendations

Like any research study, this one is not without some drawbacks. Unfortunately, convenience sampling from only one hospital restricts the study’s generalizability to other populations. Longitudinal studies and samples with a broader representation of the population must also be employed in future studies to replicate these findings in various study settings and geographic areas. However, there are several other possible variables like nutritional status, compliance with medications, and socio-demographic status, which, if considered, could have offered an even more intricate relationship between the cause and effect of fall incidences and QoL of elderly hypertensive females in Pakistan. The current study did not collect data about the amount of physical activity of participants, engagement in any exercise or systematic training, and participation in rehabilitation interventions, which have a known impact on mobility, perceptual balance confidence, and psychological well-being. In the absence of this data, it is hard to conclude the complete range of functional outcomes observed, and this may have masked residual confounding. In the future, standardized measures of physical activity and fitness should be included to further analyze whether it helps balance and functional status of older adults. The research did not address specific details on the current medication intake of the participants, especially on antihypertensive agents and psychotropic agents. Prescription programs are capable of significantly influencing balance, attentiveness, and cardiovascular steadiness, and their involvement should be addressed systematically in further study.

5. Conclusion

In Conclusion, the study attempts a preliminary effort at extending knowledge on how anthropometric measurements, PB, fear of falling, and fall risk awareness profiles contribute to the QoL of elderly hypertensive females. Thus, the knowledge of these factors may help healthcare providers come up with specific interventions that may seek to address the physical functioning, fall risk, and overall QoL of this group. A comprehensive approach to the complex factors is necessary to enhance the health of older adults and improve the condition of female hypertensive patients, specifically within the frameworks of clinical practice and public health.

Acknowledgments

The authors would like to acknowledge all the elderly females who participated in this study.

Author contributions

Conceptualization: Rikza Naseer, Anong Tantisuwat.

Data curation: Rikza Naseer.

Formal analysis: Rikza Naseer, Tsuyoshi Asai, Anong Tantisuwat.

Investigation: Anong Tantisuwat.

Methodology: Rikza Naseer, Tsuyoshi Asai, Anong Tantisuwat.

Project administration: Anong Tantisuwat.

Supervision: Anong Tantisuwat.

Visualization: Anong Tantisuwat.

Writing – original draft: Rikza Naseer.

Writing – review & editing: Tsuyoshi Asai, Anong Tantisuwat.

Correction

This article was originally published with “Rikza Naseer, PT, tDPT” spelled incorrectly as “Rikza Nazeer, PT, DPT.” It has now been corrected in the online version.