# Systematic review of candidate prognostic factors for falling in older adults identified from motion analysis of challenging walking tasks

Rosemary Dubbeldam, Yu Yuan Lee, Juliana Pennone, Luis Mochizuki, Charlotte Le Mouel

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## ► To cite this version:

Rosemary Dubbeldam, Yu Yuan Lee, Juliana Pennone, Luis Mochizuki, Charlotte Le Mouel. Systematic review of candidate prognostic factors for falling in older adults identified from motion analysis of challenging walking tasks. European Review of Aging and Physical Activity, 2023, 20 (1), pp.2. 10.1186/s11556-023-00312-9. http://hal-04043074

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Systematic review of candidate prognostic factors for falling in older adults
identified from motion analysis of challenging walking tasks

Rosemary Dubbeldam*¹, Yu Yuan Lee¹, Juliana Pennone², Luis Mochizuki², Charlotte Le Mouel¹,³

*Corresponding author: r.dubbeldam@uni-muenster.de

¹University of Münster, Institute of Sport and Exercise Science, Department of Movement Science

²University Sao Paulo, School of Physical Education and Sport

³Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006, Paris, France

ABSTRACT

The objective of this systematic review is to identify motion analysis parameters measured during challenging walking tasks which can predict fall risk in the older population. Numerous studies have attempted to predict fall risk from the motion analysis of standing balance or steady walking. However, most falls do not occur during steady gait but occur due to challenging centre of mass displacements or environmental hazards resulting in slipping, tripping or falls on stairs. We conducted a systematic review of motion analysis parameters during stair climbing, perturbed walking and obstacle crossing, predictive of fall risk in healthy older adults. We searched the databases of Pubmed, Scopus and IEEExplore.

A total of 78 articles were included, of which 62 simply compared a group of younger to a group of older adults. Importantly, the differences found between younger and older adults did not match those found between older adults at higher and lower risk of falls. Two prospective and six retrospective fall history studies were included. The other eight studies compared two groups of older adults with higher or lower risk based on mental or physical performance, functional decline, unsteadiness complaints or task performance. A wide range of parameters were reported, including outcomes related to success, timing, foot and step, centre of mass, force plates, dynamic stability, joints and segments. Due to the large variety in parameter assessment methods, a meta-analysis was not possible. Despite the range of parameters assessed, only a few candidate prognostic factors could be identified: older adults with a retrospective fall history demonstrated a significant larger **step length variability**, larger **step time variability**, and **prolonged anticipatory postural adjustments** in obstacle crossing compared to older adults without a fall history. Older adults who fell during a tripping perturbation had a **larger angular momentum** than those who did not fall. Lastly, in an obstacle course, **reduced gait flexibility** (i.e., change in stepping pattern relative to unobstructed walking) was a prognostic factor for falling in daily life. We provided recommendations for future fall risk assessment in terms of study design.

In conclusion, studies comparing older to younger adults cannot be used to explore relationships
between fall risk and motion analysis parameters. Even when comparing two older adult populations, it
is necessary to measure fall history to identify fall risk prognostic factors.

**Keywords:** Fall risk prediction, ageing, walking, stairs, obstacles, perturbations, motion analysis.
# Contents

<table><tr><td>1</td><td>Introduction</td><td>3</td></tr><tr><td>2</td><td>Methods</td><td>4</td></tr><tr><td>2.1</td><td>Literature Search</td><td>4</td></tr><tr><td>2.2</td><td>Effect size</td><td>5</td></tr><tr><td>2.3</td><td>Level of evidence</td><td>6</td></tr><tr><td>3</td><td>Results</td><td>8</td></tr><tr><td>3.1</td><td>Overview of the selected articles</td><td>8</td></tr><tr><td>3.1.1</td><td>Article extraction</td><td>8</td></tr><tr><td>3.1.2</td><td>Fall risk evaluation</td><td>8</td></tr><tr><td>3.1.3</td><td>Tasks</td><td>9</td></tr><tr><td>3.1.4</td><td>Sample size</td><td>10</td></tr><tr><td>3.1.5</td><td>Level of evidence</td><td>10</td></tr><tr><td>3.2</td><td>Motion analysis outcome parameters</td><td>11</td></tr><tr><td>3.2.1</td><td>Lack of agreement between ageing and risk studies</td><td>11</td></tr><tr><td>3.2.2</td><td>Walking, approaching, and crossing speed</td><td>12</td></tr><tr><td>3.2.3</td><td>Foot clearance</td><td>13</td></tr><tr><td>3.2.4</td><td>Step length</td><td>13</td></tr><tr><td>3.2.5</td><td>Step duration</td><td>13</td></tr><tr><td>3.2.6</td><td>Step width</td><td>14</td></tr><tr><td>3.2.7</td><td>Margin of stability</td><td>14</td></tr><tr><td>3.2.8</td><td>Outcomes assessed in less than 3 risk studies</td><td>14</td></tr><tr><td>4</td><td>Discussion</td><td>15</td></tr><tr><td>4.1</td><td>Identifying prognostic factors requires measuring fall history</td><td>16</td></tr><tr><td>4.1.1</td><td>Ageing factors do not predict fall risk</td><td>16</td></tr><tr><td>4.1.2</td><td>Risk factors in ageing</td><td>16</td></tr><tr><td>4.2</td><td>Candidate prognostic factors in challenging locomotion tasks</td><td>16</td></tr><tr><td>4.2.1</td><td>Factors correlated with prospective fall history</td><td>16</td></tr><tr><td>4.2.2</td><td>Factors correlated with retrospective fall history</td><td>17</td></tr><tr><td>4.2.3</td><td>Causes of falling</td><td>17</td></tr><tr><td>4.3</td><td>Measurement technology and setting</td><td>17</td></tr><tr><td>4.4</td><td>Recommendations</td><td>18</td></tr><tr><td>4.4.1</td><td>Study design</td><td>18</td></tr><tr><td>4.4.2</td><td>Motion parameters</td><td>18</td></tr><tr><td>4.5</td><td>Limitations</td><td>18</td></tr><tr><td>5</td><td>Conclusion</td><td>19</td></tr><tr><td>6</td><td>References</td><td>19</td></tr></table>
# 1 Introduction

Falls in older adults are frequent, with studies in numerous countries reporting fall rates between 15 and 33 % per year for older adults living in the community (Kwan et al., 2011; Peel, 2011). Fall rates increase with age, reaching 50 % for subjects aged more than 85 years (linattiniemi et al., 2009) and 60 % for those older than 90 (Fleming et al., 2008). Such falls result in injury in 15 to 45% of the cases (Mackenzie et al., 2002; Kannus et al., 2005), and pose a high economic burden for acute health care and rehabilitation (Heinrich et al., 2010).

A significant amount of research has been aimed at identifying older individuals at increased risk of falling, to orient them to appropriate prevention or rehabilitation programs. These have identified risk factors at the level of individual body functions and structures, such as decreased foot or trunk muscle strength (Mickle et al., 2009; Granacher et al., 2014), cognitive impairments and flexibility impairments (Tinetti et al., 1988; Speechley and Tinetti, 1991). Risk factors have also been identified at the level of task performance, such as walking, Timed-up-and-Go (TUG) or one limb stance (Tinetti et al., 1988; Swanenburg et al., 2010; Ihlen et al., 2018). Machine learning techniques have been used to derive fall risk prediction models, based on multiple candidate prognostic factors (Gietzelt et al., 2014; van Schooten et al., 2015; Ihlen et al., 2016; Greene et al., 2017; Silva et al., 2017; Hua et al., 2018; Rehman et al., 2020). So far, candidate prognostic factors such as step length, step time, cadence and harmonic ratio have been assessed from accelerometer signals recorded in the lab (during gait or TUG) (Greene et al., 2017; Silva et al., 2017; Hua et al., 2018; Rehman et al., 2020; Tunca et al., 2020) or in daily life (10-20 second gait bouts)(Gietzelt et al., 2014; van Schooten et al., 2015; Ihlen et al., 2018). However, the success rate for fall risk prediction varies depending on the locomotion task, with very disparate levels of reported sensitivity (55-100%), specificity (15-100%) and accuracy (62-100%) (Howcroft et al., 2013; Van Schooten et al., 2016; Montesinos et al., 2018; Rehman et al., 2020; Tunca et al., 2020).

The relatively poor performance of these fall risk prediction models may be due to fact that they rely on parameters measured during steady-state locomotion, whereas falling in real life occurs during more challenging locomotion tasks (Tinetti et al., 1988; Speechley and Tinetti, 1991; Luukinen et al., 2000; Mackenzie et al., 2002; Sartini et al., 2010). Indeed, these prospective studies indicate that most falls (60 %) occur during challenging centre of mass (CoM) displacements, such as weight transfers, standing up or sitting down, bending over, or after an external perturbation such as a push or a pull. The next
leading cause of falls is the presence of an environmental hazard (30 - 50 %), resulting in slipping, tripping, falls from an upper level (a height) or falls on stairs. Moreover, falls during such challenging locomotion tasks are related to the highest risk of severe injury i.e., fractures (Luukinen et al., 2000).

The objective of our systematic review is to determine which performance parameters assessed during challenging walking tasks are best related to falling in the older adult population. Specifically, we chose to focus on the three biomechanically challenging tasks studied in a laboratory context which are the most representative of falling in daily living: crossing obstacles, ascending and descending stairs, and external perturbations to walking.

# 2 Methods

## 2.1 Literature Search

Factors related to fall risk are ideally studied in a prospective study with older adults. As prospective studies are time-consuming, they are limited in number. Thus, cross-sectional observational studies were also included in this review. Fall risk has been related, among others, to age, to fall history and to physical and mental impairments. Therefore, this review included "ageing studies", which compare younger adults to older adults and "risk studies", which compare older adults with a higher fall risk to those with a lower fall risk, determined either prospectively, or based on fall history or mental and physical impairments.

Relevant articles should study the association between fall risk and motion analysis outcome parameters (either kinematic, kinetic, or spatial-temporal parameters). These outcome parameters should be measured during either stair climbing, perturbed walking or obstacle crossing, since these challenging walking tasks are the most related to the circumstances of falls in daily living.

The literature search was performed using the PubMed, Scopus, and IEEExplore search engines. A Boolean combination of the following terms was used to search the aforementioned databases on October 2022: (((fall) OR (fall risk)) AND ((obstacle) OR (stair) OR (perturbation)) AND ((age) OR (older) OR (elderly)) NOT ((diabetes[Title/Abstract]) OR (rheumatoid arthritis[Title/Abstract]) OR (osteoarthritis[Title/Abstract]) OR (Parkinson[Title/Abstract]) OR (stroke[Title/Abstract]))). Furthermore, all articles were published in English and no period restriction was given as a filter. The search string needed to be modified for the Scopus search since initially more than 25 000 articles were returned.
Therefore, the search string was modified such that the tasks needed to be reported in the title, the search was restricted to certain domains, certain diseases were excluded if mentioned in title or keywords, and the option to exclude certain study designs and types was used. Details on the search strings used is provided in Appendix A.

For article extraction, two reviewers screened through the titles and abstracts, then the full text. When two reviewers had opposite opinions about the inclusion of an article, a third reviewer made the final decision. The inclusion criteria were: 1) the article examined at least two groups with different fall risk (either younger and older adults, or older adults with higher and lower fall risk); 2) the article reported group differences in kinematic, kinetic or spatiotemporal parameters when performing either stair climbing, perturbed walking or obstacle crossing; and 3) all of the participants were healthy or suffering from only minor impairments corresponding to normal age degeneration, i.e., they should not suffer from any moderate to severe neurological, musculoskeletal disorder, or other conditions related to cognitive disorders and visual impairment. Exclusion criteria were: 1) the article examined the group differences in EMG or EEG signal outcomes; 2) the study compared the difference between a control group and a specific diseased or sensory impaired group, such as diabetes, arthritis, stroke, Parkinson; 3) the study design included an intervention and examined the change after the intervention; 4) dual-task studies. Dual task studies were considered outside the scope of this review, which focused on biomechanically challenging walking tasks. The influence of cognitively challenging tasks on walking performance has been reviewed elsewhere (Smith et al., 2017).

## 2.2 Effect size

The effect sizes reported by the studies were used or we calculated Cohen's D for each significant finding where effect size was not reported in the article. Cohen's D was assessed from the deviation of the mean from each group divided by the pooled standard deviation (SD). The equation of Cohen's D equation is given below (Equation 2):

$$ \text{Pooled}_{\text{SD}} = \sqrt{\frac{(SD_1^2 + SD_2^2)}{2}} \qquad \text{Equation 1} $$

$$ \text{Cohen's D} = \frac{M_1 - M_2}{\text{Pooled}_{\text{SD}}} \qquad \text{Equation 2} $$

Some studies did not report significant difference levels between groups or between repeated measures. In such cases, the minimum required sample size to reach a significance level of p = 0.05 was calculated in G*power (G*power version 3.1.9.6), based on the reported independent or dependent
group means and corresponding standard deviations. If the sample size of the groups was higher than the G\*power calculated sample size, we report the finding as a significant difference. Otherwise, we do not report the finding (neither as significant nor as non-significant).

A meta-analysis was not possible, due to the many differences within the experiments as well as in the calculation methods of the outcome parameters in the articles.

## 2.3 Level of evidence

Since multiple types of studies were included, guidelines for systematic reviews of prognostic studies (Riley et al., 2019), observational studies (Mueller et al., 2018; Dekkers et al., 2019), and non-randomised controlled trials (Sterne et al., 2016) were followed. Within these guidelines, required data extraction is similar and includes a description of the study design, participant and sample size, the experiment (a challenging walking related task), analysis method, the outcome measures, and corresponding significant findings (effect estimates).

In the above-mentioned guidelines, bias assessment includes confounding factors and covers selection and information bias, where the signalling questions to determine the bias differ per study type. Bias assessment in prognostic studies can be performed using the bias domains and corresponding signalling questions suggested in QUIPS (Hayden et al., 2013). However, these signalling questions do not cover all selection biases that may occur in observational studies, such as participant group allocation, which is better represented e.g., in the selection bias assessment of the ROBINS-I guideline. For observational studies, however, there is no agreed-upon bias assessment guideline (Mueller et al., 2018; Ma et al., 2020). To ensure the identification of all bias risks in this review, we followed the four crucial steps suggested by (Dekkers et al., 2019) and recommendations made by (Dekkers et al., 2019; Riley et al., 2019). First, a team of reviewers with experience in the field of fall risk, (para-)medical therapy, older adults, machine learning, and systematic reviews was initiated. Second, our *target trial (gold standard)* was defined as a prospective observational study of older adults including an assessment of a challenging walking task (experiment/observation) followed by a long term and repeated evaluation of the occurrence of a fall (event). Related to the research question of this review, the aim of the target trial would be to study the relationship between the occurrence of a fall and the task performance outcome measures. The assumption would be that motion analysis outcome parameters with an observed strong relationship with fall occurrence, are candidate prognostic factors for the event of a fall. Third, the effect of interest is defined as the allocation of participants to a group representing fallers (high fall risk) or non-fallers (low fall risk) and how this may influence or bias the outcome parameters.
In the fourth step, the confounding factors and bias domains were discussed and determined, and corresponding signalling questions were defined. In total, seven bias domains were defined, and they relate to potential bias issues occurring before (domain 1, 2), during (domain 3) and after (domain 4, 5, 6, 7) the effect of interest, i.e., allocation of the participants to the fall (risk) group. The first three bias domains include bias distinct from the target trial, such as bias due to confounding, bias due to selection of participants, and bias in the assessment and classification of fall risk. The confounding factors are related to both fall risk group assessment and outcome parameter: i.e., age, gender, mental and physical fitness, frailty, and fall history. Selection bias occurs when participants do not adequately represent the target population. Bias in classification occurs when participants are allocated to the wrong fall risk group, e.g., due to errors in recall or non-valid fall risk assessment methods. For articles comparing older adults at high and low risk of falling, if fall risk was assessed based on fall history, this was considered as a low risk of bias. If fall risk was assessed in another way (typically clinical tests or questionnaires), this was considered a moderate or serious risk of bias, depending on the method used. For articles comparing younger and older adults, the classification bias was considered "not applicable", and the results from these articles are presented separately. Selection and classification bias only refers to factors related to fall risk (internal validity), not to factors related to generalizability or applicability of the study (external validity).

The other 4 bias domains are independent of the study type and refer to the observation, i.e., the experiment and data handling, and include: bias due to deviations from the intended experiment, bias in the measurement of outcome parameters, bias due to missing data, and bias in the selection of the reported result. Bias due to deviations from the intended experiment may occur if fatigue differentially affects the performance of the different groups (for example, older or frailer subjects may be more fatigued towards the end of the experiment than younger or healthier subjects). The motion analysis outcome parameters may be biased if assessors are aware of group status, if different methods are used to assess outcomes in the different groups or if measurement errors are related to group status. Some of the biases from domains 4 (intended experiment) and 5 (outcome parameters) may typically be avoided using blinding. Regarding missing data, enough data should be presented in both groups to be confident of the findings and the missing data should not be group dependent. Bias in reporting the results can occur when the studies only report group means and standard deviations, but not significance level.
To make the scoring repeatable, signalling questions, corresponding sub-questions and bias examples were used (Appendix B, Table B.1). The questions were answered with: 'no', 'probably not', 'yes' or 'probably yes'. If (probably) no bias was assumed for the signalling question, we moved on to the next signalling question. If bias was assumed, for some domains, corresponding sub-questions were answered. If the signalling and sub-question could not be answered due to the lack of information in the article, the question was scored as 'no information'. Lastly, for each included article and each bias domain, each bias issue was described, scored qualitatively (low, moderate, serious, no information). The bias scoring into low, moderate, or serious bias was followed as described in QUIPS and ROBINS-I, and detailed in Appendix B, Table B.2. Bias levels were discussed and decided upon, keeping in mind to which extent and in which direction a bias factor might influence the estimated effect compared to the true effect (where the effect is the difference in outcome parameters between groups). The complete risk of bias assessment for all reviewed studies is provided in Appendix C.

In summary, for each included article the study design, included population and sample size, the experiment, the analysis method and the motion analysis outcome parameters (including both significant and non-significant results) were reported. Then, the seven bias domains were evaluated as described above.

# 3 Results

## 3.1 Overview of the selected articles

### 3.1.1 Article extraction

An overview of the systematic article extraction is given in Figure 1. In total, 2269 articles were extracted from the three databases. First, 376 duplicates were removed. Another 1790 articles were removed based on their titles and abstracts. After reading the full text, further 25 articles were removed for the following reasons: 23 articles focused on dual-tasks, static balance, single steps or steady walking, one article was a systematic review, and one article lacked the description of the participants. In the end, 78 articles were included in this review.

### 3.1.2 Fall risk evaluation

Sixteen studies compared a group of higher-risk older adults (mean age ranging from 62.5 to 81.6 years) to a group of lower-risk older adults (mean age ranging from 65.6 to 80.8 years). These studies will be referred to as **risk studies**. The details of the study designs and populations of risk studies are reported in Table 1. Two older adult performance studies assessed fall risk prospectively (Ackermans et al., 2021;
Hansson et al., 2021), by following subjects for one year to determine whether they fall, after they
performed the challenging walking task. Six studies assessed fall risk retrospectively (Brach et al., 2011;
Oh-Park et al., 2011; Uemura et al., 2011; Ackermans et al., 2019; Pieruccini-Faria and Montero-Odasso,
2019; Guadagnin et al., 2020; Gerards et al., 2021), by asking subjects at the time of the walking
measurement whether they had fallen in the previous months. Three studies evaluated risk based on
physical or mental performance at the time of the walking measurement using clinical tests or
questionnaires (Zietz et al., 2011; de Carli et al., 2014; Pan et al., 2016). Two studies evaluated risk based
on whether the subjects experienced functional decline or improvement over a one-year follow-up
(Brach et al., 2011; Oh-Park et al., 2012). One study divided the subjects into higher and lower risk
depending on whether they fell during the challenging walking task itself (Pijnappels et al., 2005). One
study compared patients with complaints of “unsteadiness” during walking (higher risk) to a group of
healthy controls without a history of falls (Chou et al., 2003). The final study compared a group of
hospitalised subjects (higher risk) with a group of healthy subjects (Brodowski et al., 2021). Within each
study, the two groups were typically age matched, except for two studies (Zietz et al., 2011; Brodowski
et al., 2021). In those two studies, the older adults at higher risk were significantly older than those at
lower risk, and this was considered a serious risk of confounding bias (Appendix C, Table C.1).

Sixty-two of the selected studies evaluated fall risk based solely on age, comparing a group of younger
(mean age ranging from 20.9 to 29.3 years) and a group of older participants (mean age ranging from
55.6 to 81 years). These studies will be referred to as **ageing studies** in the rest of the text. The details of
the study designs and populations of ageing studies are reported in Appendix D. The full list of
references for ageing studies is in Appendix E.

3.1.3 Tasks

Task characteristics are also reported in Table 1 for risk studies and Appendix D for ageing studies.

Stair climbing was assessed in 7 risk studies and 19 ageing studies. The stairs had a variety of
configurations, ranging from a single (Begg and Sparrow, 2000; Crosbie and Gan, 2003) to 13 steps
(Brodowski et al., 2021) and the studies evaluated either ascent, descent or both.

Perturbed walking was assessed in 2 risk studies and 20 ageing studies. Most perturbations were applied
through a translation of the support surface (14 studies), either in the mediolateral or anteroposterior
direction, or both. The other types of perturbations were waist-pulls (Laudani et al., 2021; Rum et al.,
2021), ankle pull (McCrum et al., 2016; Bosquée et al., 2021), tripping (Pijnappels et al., 2005), visual
perturbations (Sun et al., 2017; Kazanski et al., 2020), soapy water (Liu and Lockhart, 2009) and surface drop (Jeon et al., 2022).

Obstacle crossing was studied in 7 risk and 23 ageing studies. The obstacles used had a variety of dimensions, with a height ranging from 0 cm (visually projected obstacles in (Caetano et al., 2016) and (Chen et al., 1994) up to 30 cm (Pieruccini-Faria and Montero-Odasso, 2019; Hansson et al., 2021) or 30% of the leg length (Lu et al., 2006; Huang et al., 2008; Oh-Park et al., 2012; Park and Lee, 2012; Pan et al., 2016). In the prospective study by Hansson et al., the participants navigated an obstacle course comprising several tasks in sequence: standing up from a chair, walking along a narrow path of 25 cm width for 3 meters, walking over an uneven surface, crossing 3 obstacles of 30 cm height, and climbing up and down a stair of 10 steps (Hansson 2021).

In 62 out of 78 studies, subjects were allowed to walk at their preferred velocity.

Most studies used either an infrared camera system (33 studies), force platforms (10 studies), or both (28 studies) to measure walking parameters. One study used an inertial measurement unit (IMU) attached to the right thigh to measure kinematic parameters (Hansson et al., 2021).

### 3.1.4 Sample size

The selected studies had a wide range of sample sizes, with 30 studies having 10 or fewer participants in one of the groups, and 2 studies having more than 370 participants in total (Oh-Park et al., 2011, 2012). Overall, risk studies assessed 680 older participants with a higher risk and 927 with a lower risk. Ageing studies assessed 921 younger participants and 1065 older participants.

### 3.1.5 Level of evidence

An overview of the risk of bias for each domain and article is given in Appendix C. Based on the seven risk of bias domains, we classified 23 studies with low risk of bias, 48 studies with moderate risk of bias and 7 studies with serious risk of bias. Typical biases among the articles included in the seven domains were:

1. Confounding factors: confounding due to a difference in the gender ratio between groups (9 studies); no information on the gender ratio between groups (18 studies).

2. Participant selection: the health status of the participants (physical health, mental health or fall history) was used an exclusion criterion (15 studies); the study population had an unbalanced gender ratio (32 studies, including 11 which included either only females or only males).
3. Group allocation: risk was evaluated based on balance tests or clinical tests or questionnaires rather than prospective or retrospective fall history (6 studies)

4. Intended experiment: no information on either randomization or fatigue prevention (42 studies)

5. Outcome parameters: invalid assessment of centre of mass location (3 studies)

6. Missing data: missing data due to differences or errors in task performance resulting in unbalanced groups for analysis (10 studies).

7. Result reporting: significance level was not reported (4 studies).

## 3.2 Motion analysis outcome parameters

A wide range of parameters were reported by the studies, including outcomes related to success, timing, foot and step, centre of mass, force plates, dynamic stability, joints and segments. Table 2 presents the subset of outcome parameters that were reported for at least one risk study, and indicates the studies reporting either significant or non-significant findings for each outcome. Outcome parameters that were only reported in ageing studies are listed in Appendix F. The most commonly reported parameters were step length (7 risk and 31 ageing studies), stance, swing and compensatory duration (6 risk and 23 ageing studies) and walking, approaching or crossing speed (11 risk and 20 ageing studies). Thirty-seven parameters were reported by a single study in a single task.

### 3.2.1 Lack of agreement between ageing and risk studies

An important finding is that outcomes which were significantly different between younger and older adults were not necessarily good prognostic factors for fall risk (Table 2). This finding was particularly robust when considering outcomes reported by a large number of studies.

The most reported finding was step length, which was found to be significantly shorter between younger and older adults in a majority of studies (Table 2) for stairs (3 out of 4 studies), perturbations (9 out of 12 studies) and obstacles (10 out of 15 studies). In contrast, step length was not significantly different between older adults at higher and lower risk in either stairs (Zietz et al., 2011), perturbations (Pijnappels et al., 2005; Gerards et al., 2021) or obstacles (Chou et al., 2003; Pan et al., 2016; Guadagnin et al., 2020; Gerards et al., 2021). This finding was consistent whether risk was evaluated based on retrospective fall history (Pieruccini-Faria and Montero-Odasso, 2019; Guadagnin et al., 2020; Gerards et al., 2021), balance performance (Pijnappels et al., 2005), physical and mental level (Zietz et al., 2011; Pan et al., 2016) or unsteadiness complaints (Chou et al., 2003).
Another commonly reported finding was speed when walking over obstacles. The speed was found to be significantly higher in younger than older subjects in 8 out of the 10 studies which assessed this parameter (Table 2). In contrast, no significant difference in obstacle walking speed between older adults at higher and lower risk was found in any of the 5 studies that assessed this parameter, whether risk was evaluated based on retrospective fall history (Pieruccini-Faria and Montero-Odasso, 2019; Guadagnin et al., 2020), physical level (Pan et al., 2016), unsteadiness complaints (Chou et al., 2003) or walking decline over a year (Brach et al., 2011).

Since the goal of this review was to identify candidate fall risk prognostic factors for older subjects, in the rest of this result section we only report the results from risk studies.

### 3.2.2 Walking, approaching, and crossing speed

In most studies (62 out of 78), subjects were allowed to walk at self-selected speed. Walking, approaching or crossing speed was assessed in 5 risk studies with stairs, 1 risk study with perturbations and 5 risk studies with obstacles. Stair studies reported speed in number of steps per second (Brach et al., 2011; Ackermans et al., 2019, 2021) and two studies reported the total stair ascent or descent time (Oh-Park et al., 2011, 2012). The perturbation study reported speed in meters per second (Pijnappels et al., 2005). Three obstacle studies used multiple steps (including the obstacle crossing steps) to assess an average walking speed, either over several meters or over 6 steps (Brach et al., 2011; Pieruccini-Faria and Montero-Odasso, 2019; Guadagnin et al., 2020). One obstacle study reported the crossing speed of the single stride over the obstacle (Pan et al., 2016).

As mentioned previously, none of the obstacle studies found a significant difference in speed between older adults at higher and lower risk (Figure 2). While subjects significantly slowed down when crossing obstacles with increasing height, this did not differ significantly between fall risk groups (Pan et al., 2016). One study reported the decrease in walking speed when walking over an obstacle compared to normal walking (Brach et al., 2011). Whereas the walking speed over the obstacle itself was not significantly different across groups, the decrease in speed compared to baseline was significantly larger for older subjects whose walking speed declined over a one-year follow-up (Brach et al., 2011). Note that in this study, retrospective fall history was also assessed, and was not significantly different between subjects whose walking speed improved or declined over one year.

Walking speed on stairs was not found to be significantly different between older adults at higher and lower risk when fall risk was assessed prospectively (Ackermans et al., 2021) or retrospectively (Oh-Park et al., 2011; Ackermans et al., 2019). In contrast, older adults at higher risk were found to be slower on
stairs when risk was evaluated based on functional decline (Oh-Park et al., 2012) or when comparing a group of older (77.3 ± 7.8 years) hospitalised patients to a group of healthy participants (70.3 ± 5.3 years) (Brodowski et al., 2021). The variance in walking speed was also found to be not significantly different between higher and lower risk older adults (Ackermans et al., 2019, 2021).

### 3.2.3 Foot clearance

Foot clearance was assessed in 3 risk studies with obstacles and 3 risk studies with stairs, and was found to be not significantly different between older adults at higher and lower risk when fall risk was assessed prospectively (Ackermans et al., 2021), retrospectively (Ackermans et al., 2019; Guadagnin et al., 2020) or based on unsteadiness complaints (Chou et al., 2003). In contrast, subjects with lower physical level have an increased foot clearance on obstacles and reduced symmetry in foot clearance (Pan et al., 2016). On stairs, older subjects (79.3± 6.4 years) with lower physical and mental level have a reduced foot clearance compared to old subjects (72.1 ± 3.8 years) with higher physical and mental level (Zietz et al., 2011). However, variance in foot clearance was not significantly correlated with fall risk assessed either based on retrospective fall history (Ackermans et al., 2019) or physical and mental level (Zietz et al., 2011).

### 3.2.4 Step length

Step length was assessed in 1 risk study with stairs, 2 with perturbations and 4 with obstacles. It was not found to be significantly different between older adults at higher and lower risk, evaluated based on retrospective fall history (Pieruccini-Faria and Montero-Odasso, 2019; Guadagnin et al., 2020; Gerards et al., 2021), physical and/or mental level (Zietz et al., 2011; Pan et al., 2016), balance performance (Pijnappels et al., 2005) or unsteadiness complaints (Chou et al., 2003). In perturbed walking, one study reported that in trips leading to a fall, the foot was placed backwards of the pelvis during the recovery step, whereas it was placed forwards for trials in which the subjects recovered balance (Pijnappels et al., 2005). The study did not report whether this difference in foot placement was significant. When crossing an obstacle, **step length variability** was found to be larger in older adults with a retrospective fall history (Pieruccini-Faria and Montero-Odasso, 2019), whereas it was not significantly different between groups during unobstructed walking.

### 3.2.5 Step duration

Stance, swing, initiation, reaction, recovery, or compensatory duration were assessed in 1 risk study with stairs, 2 with perturbations and 3 with obstacles. During stair ascent (but not descent), the support phase was significantly longer for older adults with a lower physical level (Carli et al., 2014). When
walking with perturbations, the step duration (Gerards et al., 2021), stance phase and double support
durations (Pijnappels et al., 2005) were not significantly different between older adults at higher and
lower risk, evaluated based either on retrospective fall history (Gerards et al., 2021) or the subject's
ability to recover their balance after the perturbation (Pijnappels et al., 2005). When crossing an
obstacle, step duration increased compared to unobstructed walking, but there was no difference
between subjects at higher and lower risk based on retrospective fall history (defined as at least one
injurious fall or at least two non-injurious falls) (Pieruccini-Faria and Montero-Odasso, 2019) or
unsteadiness complaints (Chou 2003). Subjects with a more severe fall history had a significantly **higher**
step time variability when walking over an obstacle, but not during unobstructed walking (Pieruccini-
Faria and Montero-Odasso, 2019). When crossing an obstacle, swing time was not significantly different
between older adults with higher or lower physical level (Pan et al., 2016). Furthermore, the average
stride time and stride time variability when crossing an obstacle course (standing up from chair, walking
along narrow path, walking on an uneven surface, crossing 3 obstacles, then either climbing stairs or
ending the task) was not significantly different between older adults with and without a prospective fall
history (Hansson et al., 2021).

3.2.6 Step width

Step width was assessed in 1 risk study with stairs, 1 with perturbations and 2 with obstacles. When crossing an obstacle, a smaller step width for all obstacle heights was reported for older adults at lower physical level (Pan et al., 2016). In contrast, there was no significant difference in step width between older patients with unsteadiness complaints and healthy controls (Chou 2003). When walking with perturbations, there was no significant difference between older adults with and without a retrospective fall history (Gerards et al., 2021). When climbing stairs, there were no significant differences in step width or step width variability between older adults with a higher and lower physical level (Zietz et al., 2011).

3.2.7 Margin of stability

Margin of stability was assessed in 3 risk studies with stairs and 1 with perturbations, and was found to be not significantly correlated to prospective (Ackermans et al., 2021) or retrospective (Ackermans et al., 2019; Gerards et al., 2021) fall history, or physical and mental level (Zietz et al., 2011).

3.2.8 Outcomes assessed in less than 3 risk studies

When walking on stairs, there were no significant differences in either the percent of the foot surface in
contact with the stairs, its variance, or the required coefficient of friction, between older adults at higher
and lower risk, evaluated based either on prospective (Ackermans et al., 2021) or retrospective fall history (Ackermans et al., 2019). A cluster analysis combining multiple parameters was able to identify different stair negotiation strategies. However, these strategies could not predict the risk of falling on stairs. For example, older adults with a more conservative strategy for stair descent (i.e. increased foot clearance) have a similar hazard risk to those who adopt a riskier strategy (i.e. reduced foot clearance).

When walking on stairs, subjects with a lower physical level had a significantly lower peak vertical ground reaction force, lower vertical loading and unloading rate, and higher vertical impulse (Carli et al., 2014). Center of mass acceleration and variance were not significantly different between older adults with a higher or lower physical and mental level (Zietz et al., 2011).

Older subjects who fell when they are tripped during walking had **higher angular momentum** compared to those who recovered their balance (Pijnappels et al., 2005). There was however no significant difference in the ankle, knee or hip moments or in hip motion.

When crossing an obstacle, foot placement relative to the obstacle was not significantly different between older adults at higher and lower risk, evaluated based either on retrospective fall history (Guadagnin et al., 2020) or physical level (Pan et al., 2016). Moreover, the medial-lateral foot excursion did not differ between older patients with unsteadiness complaints and healthy controls (Chou 2003). When initiating walking over an obstacle, the **duration of the anticipatory postural adjustments was longer** in older adults with a retrospective fall history (Uemura et al., 2011), but not in unobstructed walking. Lastly, the medial-lateral CoM range of motion and peak velocity were significantly higher in older adults suffering from unsteadiness complaints compared to healthy older adults (Chou 2003).

When navigating an obstacle course, **gait flexibility** was reduced in older adults with a prospective fall history (Hansson et al., 2021). In this study, gait flexibility was defined as the difference in the stepping signal from an IMU above the knee between unobstructed walking and navigating the obstacle course.

# 4 Discussion

While many studies have investigated fall risk during steady walking, this task is not comparable to the majority situations in which older people fall in daily life (Tinetti et al., 1988; Speechley and Tinetti, 1991; Luukinen et al., 2000; Mackenzie et al., 2002; Sartini et al., 2010). This is the first systematic review of the performance of younger and older adults with higher and lower fall risk during challenging walking tasks: stair climbing, perturbed walking and obstacle crossing. We identified several motion
analysis performance parameters assessed during challenging walking tasks that may be possible
candidates to predict the risk of falling in the older population.

4.1 Identifying prognostic factors requires measuring fall history

4.1.1 Ageing factors do not predict fall risk

Most of the articles identified by our search strategy (62 out of 78) simply compared healthy younger to healthy older adults. Our results show that the observed differences between younger and older adults are not necessarily good prognostic factors for fall risk in the older population. For example, walking speed is significantly smaller in older than younger adults, but it is not significantly correlated with either prospective (Ackermans et al., 2021) or retrospective (Oh-Park et al., 2011; Ackermans et al., 2019; Pieruccini-Faria and Montero-Odasso, 2019; Guadagnin et al., 2020) fall history. This is consistent with the findings from perturbed stance. There are robust differences in the responses of younger versus older subjects to stance perturbations (Woollacott et al., 1986; Inglin and Woollacott, 1988; Nardone et al., 1995; Allum et al., 2004; Tokuno et al., 2010). However, the response to stance perturbations typically do not differ between older adults with and without a prospective (Baloh et al., 1998; Hill et al., 1999; Kario et al., 2001) or retrospective (Studenski et al., 1991) fall history. Therefore, studies comparing older to younger adults cannot be used to explore relationships between fall risk and motion analysis parameters.

4.1.2 Risk factors in ageing

In the 16 remaining studies which compared two groups of older adults, only 2 studies classified the
groups according to prospective fall history, and 6 studies according to retrospective fall history. The
remaining 8 studies used a variety of methods to distinguish between higher and lower risk older adults.
Importantly, the findings from these latter studies are not always corroborated by the studies which
classified older adults according to fall history. For example, walking speed on stairs is not significantly
correlated with fall history (Oh-Park et al., 2011; Ackermans et al., 2019, 2021), but it is reduced in
subjects who then undergo functional decline (Oh-Park et al., 2012) and in hospitalised patients relative
to healthy subjects (Brodowski et al., 2021). Candidate prognostic factors identified from cross-sectional
studies which do not measure fall history must therefore be interpreted with caution.

4.2 Candidate prognostic factors in challenging locomotion tasks

4.2.1 Factors correlated with prospective fall history

When navigating an obstacle course, **gait flexibility** (the change in stepping pattern relative to unobstructed walking) may be a good prognostic factor for fall risk (Hansson et al., 2021). When walking
up and down stairs, no single motion analysis parameter is able to predict subsequent hazard events (including falls) on stairs (Ackermans et al., 2021). However, a **cluster analysis** using several parameters may be useful to identify different stair negotiation strategies (Ackermans et al., 2021). This may be useful to identify how older subjects alter their stair negotiation strategy, either to compensate other deficits, or in response to fear of falling (Anders et al., 2007; Donoghue et al., 2013).

### 4.2.2 Factors correlated with retrospective fall history

In obstacle crossing, older adults with a retrospective fall history demonstrated a **larger step length variability and step time variability** (Pieruccini-Faria and Montero-Odasso, 2019). When initiating walking over an obstacle, **anticipatory postural adjustments are prolonged** in older adults with a retrospective fall history (Uemura et al., 2011). Prolonged reaction times have also been found to be correlated to prospective fall history when subjects are asked to perform a single step in response to a cue (Melzer et al., 2010; Pijnappels et al., 2010). Prolonged stepping times may however be specific to tasks in which the step is self-initiated in response to an external cue. Indeed, when the step is a response to an external perturbation, stepping initiation is earlier in subjects with a prospective fall history (Mille et al., 2013).

### 4.2.3 Causes of falling

Most studies focussed on spatiotemporal outcome parameters. Only a limited number of studies tried to relate these parameters and fall risk to other underlying mechanisms such as postural adjustments or angular moment. This may however be a promising avenue for future research. Indeed, as mentioned above, postural adjustments have been related to retrospective fall history (Uemura et al., 2011).

Moreover, in one study design, a perturbation was used which caused participants to trip, and fall in a portion of the trials (Pijnappels et al., 2005). This allowed the authors to identify that in trips leading to a fall, the angular momentum was reduced. While spatiotemporal parameters are relatively easy to assess, fall prevention requires a better understanding of the mechanisms underlying poor performance in challenging walking tasks. Measuring the mechanisms which underlie falls (such as angular momentum) requires assessing external forces and full-body kinematics with advanced measurement technologies such as load or pressure plates and 3-dimensional movement capture systems.

## 4.3 Measurement technology and setting

All the included studies were performed in a laboratory setting, mostly using traditional motion capture.
However, fall risk may be better identified by measuring subjects in their natural environment. Indeed,
factors such as ambient lighting, physical or mental fatigue, and stair, obstacle or perturbation type can
play a role in increasing fall risk (Startzell et al., 2000; Jacobs, 2016). Inertial Measurement Units (IMU's) enable ambulant measurements and could be used to monitor stair climbing or obstacle crossing. An IMU sensor on the sacrum (Bolink et al., 2016; Jacobs, 2016; Bartlett and Goldfarb, 2018) or thigh (Hansson et al., 2021) could be used to measure relevant motion parameters, such as gait flexibility, step time or step length variability. Changes in gait initiation can also be detected in ambulatory settings using IMUs (Mancini et al., 2016; King et al., 2017). Such parameters measured during challenging ambulatory tasks could then be incorporated into current fall risk prediction models to improve accuracy, sensitivity, and specificity of fall predictions (Montesinos et al., 2018).

## 4.4 Recommendations

### 4.4.1 Study design

To determine prognostic factors for falling, fall history must be measured. If possible, this should be done in a longitudinal design with a long-term recording of fall history. When this is not feasible, fall history may alternatively be measured retrospectively. To avoid bias in the results, the subjects must be well described in terms of mental and physical fitness level, and must represent the diversity within the older population living in the community in terms of fall risk, gender, and fitness level. Furthermore, group sample sizes need to be sufficiently large to ensure statistical power.

### 4.4.2 Motion parameters

The findings suggest that fall risk can be better discriminated from changes or variability in outcome parameters rather than the mean of a given parameter. For example, whereas step time and step length are not correlated with retrospective fall history, their variability is (Pieruccini-Faria and Montero-Odasso, 2019). Similarly, gait flexibility (the change in stepping pattern between unobstructed walking and navigating an obstacle course) is prognostic of falling (Hansson et al., 2021). Moreover, parameters related to the cause of falls such as postural adjustments (Uemura et al., 2011) or angular momentum (Pijnappels et al., 2005) may be relevant to identify fall risk. Also, combinations of parameters may be used to identify the strategies employed by older adults when faced with challenging walking tasks (Ackermans et al., 2019, 2021). Finally, to be able to compare studies in a meta-analysis, in the future motion analysis parameters should be assessed in a uniform way.

## 4.5 Limitations

This review was restricted to 'healthy' older adults, i.e., older adults with normal ageing degeneration impairments and no moderate to severe diseases significantly impairing locomotion. Therefore, our results do not apply to older adults suffering from diseases impairing their locomotion such as severe
osteoarthritis, stroke or Parkinson disease. Moreover, the review focussed on three challenging walking tasks, which were related to biomechanics risk factors for falling. Other potentially relevant tasks (such as running or dual tasks) were not considered. Due to the task specificity of balance, the findings may not generalise to other tasks.

Finally, although 78 articles were included in this study, fall history was measured in only 8 of these.

Moreover, the large variation in assessed outcome parameters resulted in mostly a limited number of studies reporting any given parameter. Therefore, our motion parameter recommendations are based on a very limited number of studies.

## 5 Conclusion

We investigated the relationship between fall risk among the older population and their performance during challenging walking tasks (stair climbing, perturbations and obstacle crossing). The results from the 78 included studies indicated that findings from studies comparing young to older adults cannot be used as prognostic factors for fall risk. Even when comparing two older adult populations, it is necessary to measure fall history so as to identify fall risk prognostic factors. We identified candidate motion analysis factors for fall risk prediction, which could also be assessed ambulatory in a more natural environment. Finally, we provided recommendations for the study design and motion parameters to be assessed in future fall risk assessment studies.

## 6 References

Ackermans, T., Francksen, N., Lees, C., Papatzika, F., Arampatzis, A., Baltzopoulos, V., et al. (2021). Prediction of Balance Perturbations and Falls on Stairs in Older People Using a Biomechanical Profiling Approach: A 12-Month Longitudinal Study. *Journals of Gerontology - Series A Biological Sciences and Medical Sciences* 76, 638–646. doi: 10.1093/gerona/glaa130.

Ackermans, T. M. A., Francksen, N. C., Casana-eslava, R. V., Lees, C., Baltzopoulos, V., Lisboa, P. J. G., et al. (2019). A novel multivariate approach for biomechanical profiling of stair negotiation. *Experimental Gerontology* 124, 110646. doi: 10.1016/j.exger.2019.110646.

Allum, J. H. J., Carpenter, M. G., Honegger, F., Adkin, A. L., and Bloem, B. R. (2004). Age-dependent variations in the directional sensitivity of balance corrections and compensatory arm movements in man. *The Journal of Physiology* 542, 643–663. doi: 10.1113/jphysiol.2001.015644.

Anders, J., Dapp, U., Laub, S., and von Renteln-Kruse, W. (2007). Impact of fall risk and fear of falling on mobility of independently living senior citizens transitioning to frailty: screening results concerning fall prevention in the community. *Zeitschrift für Gerontologie und Geriatrie* 40, 255–267.
Baloh, R. W., Corona, S., Jacobson, K. M., Enrietto, J. A., and Bell, T. (1998). A Prospective Study of Posturography in Normal Older People. *Journal of the American Geriatrics Society* 46, 438–443.

Bartlett, H. L., and Goldfarb, M. (2018). A phase variable approach for IMU-based locomotion activity recognition. *IEEE Transactions on Biomedical Engineering* 65, 1330–1338. doi: 10.1109/TBME.2017.2750139.

Begg, R. K., and Sparrow, W. A. (2000). Gait characteristics of young and older individuals negotiating a raised surface: Implications for the prevention of falls. *Journals of Gerontology - Series A Biological Sciences and Medical Sciences* 55, 147–154. doi: 10.1093/gerona/55.3.M147.

Bolink, S. A. A. N., Naisas, H., Senden, R., Essers, H., Heyligers, I. C., Meijer, K., et al. (2016). Validity of an inertial measurement unit to assess pelvic orientation angles during gait, sit-stand transfers and step-up transfers: Comparison with an optoelectronic motion capture system. *Medical Engineering and Physics* 38, 225–231. doi: 10.1016/j.medengphy.2015.11.009.

Bosquée, J., Werth, J., Epro, G., Hülsdünker, T., Potthast, W., Meijer, K., et al. (2021). The ability to increase the base of support and recover stability is limited in its generalisation for different balance perturbation tasks. *European Review of Aging and Physical Activity* 18, 1–10. doi: 10.1186/s11556-021-00274-w.

Brach, J. S., Perera, S., VanSwearingen, J. M., Hile, E. S., Wert, D. M., and Studenski, S. A. (2011). Challenging Gait Conditions Predict 1-Year Decline in Gait Speed in Older Adults With Apparently Normal Gait. *Phys Ther* 91, 1857–1864. doi: 10.2522/ptj.20100387.

Brodowski, H., Andres, N., Gumny, M., Eicher, C., Steinhagen-Thiessen, E., Tannen, A., et al. (2021). Reliability of stair-climbing speed in two cohorts of older adults. *International Journal of Therapy and Rehabilitation* 28, 1–15. doi: 10.12968/ijtr.2020.0137.

Caetano, M. J. D., Lord, S. R., Schoene, D., Pelicioni, P. H. S., Sturnieks, D. L., and Menant, J. C. (2016). Age-related changes in gait adaptability in response to unpredictable obstacles and stepping targets. *Gait and Posture* 46, 35–41. doi: 10.1016/j.gaitpost.2016.02.003.

Carli, J. V. M. de, Bohrer, R. C. D., Lodovico, A., and Rodacki, A. L. F. (2014). Kinetic analysis of floor-to-stair transition gait of elderly with different functional levels. *Rev. bras. cineantropom. desempenho hum.* 16, 66–75. doi: 10.5007/1980-0037.2014v16n1p66.

Chen, H. C., Ashton-Miller, J. A., Alexander, N. B., and Schultz, A. B. (1994). Effects of age and available response time on ability to step over an obstacle. *J Gerontol* 49, M227-233. doi: 10.1093/geronj/49.5.m227.

Chou, L.-S., Kaufman, K. R., Hahn, M. E., and Brey, R. H. (2003). Medio-lateral motion of the center of mass during obstacle crossing distinguishes elderly individuals with imbalance. *Gait Posture* 18, 125–133. doi: 10.1016/s0966-6362(02)00067-x.

Crosbie, J., and Gan, N. (2003). Effect of age and visual contrast on gait during obstacle negotiation. *Australasian Journal on Ageing* 22, 131–135. doi: 10.1111/j.1741-6612.2003.tb00483.x.
de Carli, J. V. M., Bohrer, R. C. D., Lodovico, A., and Rodacki, A. L. F. (2014). Kinetic analysis of floor-to-stair transition gait of elderly with different functional levels. *Revista Brasileira de Cineantropometria e Desempenho Humano* 16, 66-75. doi: 10.5007/1980-0037.2014v16n1p66.

Dekkers, O. M., Vandenbroucke, J. P., Cevallos, M., Renehan, A. G., Altman, D. G., and Egger, M. (2019). COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology. *PLoS Medicine* 16, 1-24. doi: 10.1371/journal.pmed.1002742.

Donoghue, O. A., Cronin, H., Savva, G. M., O'Regan, C., and Kenny, R. A. (2013). Effects of fear of falling and activity restriction on normal and dual task walking in community dwelling older adults. *Gait & posture* 38, 120-124.

Fleming, J., Matthews, F. E., and Brayne, C. (2008). Falls in advanced old age: recalled falls and prospective follow-up of over-90-year-olds in the Cambridge City over-75s Cohort study. *BMC Geriatr* 8, 6. doi: 10.1186/1471-2318-8-6.

Gerards, M. H. G., Meijer, K., Karamanidis, K., Grevendonk, L., Hoeks, J., Lenssen, A. F., et al. (2021). Adaptability to Balance Perturbations During Walking as a Potential Marker of Falls History in Older Adults. *Frontiers in Sports and Active Living* 3. doi: 10.3389/fspor.2021.682861.

Gietzelt, M., Feldwieser, F., Gövercin, M., Steinhagen-Thiessen, E., and Marschollek, M. (2014). A prospective field study for sensor-based identification of fall risk in older people with dementia. *Informatics for Health and Social Care* 39, 249-261. doi: 10.3109/17538157.2014.931851.

Granacher, U., Lacroix, A., Roettger, K., Gollhofer, A., and Muehlbauer, T. (2014). Relationships between trunk muscle strength, spinal mobility, and balance performance in older adults. *Journal of Aging and Physical Activity* 22, 490-498. doi: 10.1123/JAPA.2013-0108.

Greene, B. R., Redmond, S. J., and Caulfield, B. (2017). Fall Risk Assessment Through Automatic Combination of Clinical Fall Risk Factors and Body-Worn Sensor Data. *IEEE Journal of Biomedical and Health Informatics* 21, 725-731. doi: 10.1109/JBHI.2016.2539098.

Guadagnin, E. C., Da Rocha, E. S., Bobbert, M. F., Duysens, J., and Carpes, F. P. (2020). Do older women with a fall history cross obstacles differently than older women with no fall history? *Human Movement* 21, 47-53. doi: 10.5114/hm.2020.91345.

Hansson, E. E., Valkonen, E., Olsson Möller, U., Chen Lin, Y., Magnusson, M., and Fransson, P.-A. (2021). Gait Flexibility among Older Persons Significantly More Impaired in Fallers Than Non-Fallers-A Longitudinal Study. *Int J Environ Res Public Health* 18, 7074. doi: 10.3390/ijerph18137074.

Hayden, J. A., van der Windt, D. A., Cartwright, J. L., Côté, P., and Bombardier, C. (2013). Assessing bias in studies of prognostic factors. *Annals of Internal Medicine* 158, 280-286. doi: 10.7326/0003-4819-158-4-201302190-00009.

Heinrich, S., Rapp, K., Rissmann, U., Becker, C., and König, H. H. (2010). Cost of falls in old age: A systematic review. *Osteoporosis International* 21, 891-902. doi: 10.1007/s00198-009-1100-1.
Hill, K., Schwarz, J., Flicker, L., and Carroll, S. (1999). Falls among healthy, community-dwelling, older women: a prospective study of frequency, circumstances, consequences and prediction accuracy. *Australian and New Zealand Journal of Public Health* 23, 41–48.

Howcroft, J., Kofman, J., and Lemaire, E. D. (2013). Review of fall risk assessment in geriatric populations using inertial sensors. 1–12.

Hua, A., Quicksall, Z., Di, C., Motl, R., LaCroix, A. Z., Schatz, B., et al. (2018). Accelerometer-based predictive models of fall risk in older women: a pilot study. *npj Digital Medicine* 1. doi: 10.1038/s41746-018-0033-5.

Huang, S. C., Lu, T. W., Chen, H. L., Wang, T. M., and Chou, L. S. (2008). Age and height effects on the center of mass and center of pressure inclination angles during obstacle-crossing. *Medical Engineering and Physics* 30, 968–975. doi: 10.1016/j.medengphy.2007.12.005.

Ihlen, E. A. F., van Schooten, K. S., Bruijn, S. M., van Dieën, J. H., Vereijken, B., Helbostad, J. L., et al. (2018). Improved prediction of falls in community-dwelling older adults through phase-dependent entropy of daily-life walking. *Frontiers in Aging Neuroscience* 10, 1–12. doi: 10.3389/fnagi.2018.00044.

Ihlen, E. A. F., Weiss, A., Bourke, A., Helbostad, J. L., and Hausdorff, J. M. (2016). The complexity of daily life walking in older adult community-dwelling fallers and non-fallers. *Journal of Biomechanics* 49, 1420–1428. doi: 10.1016/j.jbiomech.2016.02.055.

linattiniemi, S., Jokelainen, J., and Luukinen, H. (2009). Falls risk among a very old home-dwelling population. *Scand J Prim Health Care* 27, 25–30. doi: 10.1080/02813430802588683.

Inglin, B., and Woollacott, M. (1988). Age-related changes in anticipatory postural adjustments associated with arm movements. *J Gerontol* 43, M105-113.

Jacobs, J. V. (2016). A review of stairway falls and stair negotiation: Lessons learned and future needs to reduce injury. *Gait and Posture* 49, 159–167. doi: 10.1016/j.gaitpost.2016.06.030.

Jeon, W., Whitall, J., and Westlake, K. (2022). Age-related differences in stepping stability following a sudden gait perturbation are associated with lower limb eccentric control of the perturbed limb. *Experimental Gerontology* 167, 111917. doi: 10.1016/j.exger.2022.111917.

Kannus, P., Sievänen, H., Palvanen, M., Järvinen, T., and Parkkari, J. (2005). Prevention of falls and consequent injuries in elderly people. *Lancet* 366, 1885–1893. doi: 10.1016/S0140-6736(05)67604-0.

Kario, K., Tobin, J. N., Wolfson, L. I., Whipple, R., Derby, C. A., Singh, D., et al. (2001). Lower standing systolic blood pressure as a predictor of falls in the elderly: a community-based prospective study. *Journal of the American College of Cardiology* 38, 246–252.

Kazanski, M. E., Cusumano, J. P., and Dingwell, J. B. (2020). How healthy older adults regulate lateral foot placement while walking in laterally destabilizing environments. *Journal of Biomechanics* 104, 109714. doi: 10.1016/j.jbiomech.2020.109714.
King, L. A., Mancini, M., Fino, P. C., Chesnutt, J., Swanson, C. W., Markwardt, S., et al. (2017). Sensor-Based Balance Measures Outperform Modified Balance Error Scoring System in Identifying Acute Concussion. *Annals of Biomedical Engineering* 45, 2135-2145. doi: 10.1007/s10439-017-1856-y.

Kwan, M. M.-S., Close, J. C. T., Wong, A. K. W., and Lord, S. R. (2011). Falls incidence, risk factors, and consequences in Chinese older people: a systematic review. *J Am Geriatr Soc* 59, 536-543. doi: 10.1111/j.1532-5415.2010.03286.x.

Laudani, L., Rum, L., Valle, M. S., Macaluso, A., Vannozzi, G., and Casabona, A. (2021). Age differences in anticipatory and executory mechanisms of gait initiation following unexpected balance perturbations. *European Journal of Applied Physiology* 121, 465-478. doi: 10.1007/s00421-020-04531-1.

Liu, J., and Lockhart, T. E. (2009). Age-related joint moment characteristics during normal gait and successful reactive-recovery from unexpected slip perturbations. *Gait Posture* 30, 276-281. doi: 10.1016/j.gaitpost.2009.04.005.

Lu, T. W., Chen, H. L., and Chen, S. C. (2006). Comparisons of the lower limb kinematics between young and older adults when crossing obstacles of different heights. *Gait and Posture* 23, 471-479. doi: 10.1016/j.gaitpost.2005.06.005.

Luukinen, H., Herala, M., Koski, K., Honkanen, R., Laippala, P., and Kivelä, S. L. (2000). Fracture risk associated with a fall according to type of fall among the elderly. *Osteoporosis International* 11, 631-634. doi: 10.1007/s001980070086.

Ma, L. L., Wang, Y. Y., Yang, Z. H., Huang, D., Weng, H., and Zeng, X. T. (2020). Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: What are they and which is better? *Military Medical Research* 7, 1-11. doi: 10.1186/s40779-020-00238-8.

Mackenzie, L., Byles, J., and Higginbotham, N. (2002). A prospective community-based study of falls among older people in Australia: Frequency, circumstances, and consequences. *OTJR Occupation, Participation and Health* 22, 143-152. doi: 10.1177/153944920202200403.

Mancini, M., Chiari, L., Holmstrom, L., Salarian, A., and Horak, F. B. (2016). Validity and reliability of an IMU-based method to detect APAs prior to gait initiation. *Gait and Posture* 43, 125-131. doi: 10.1016/j.gaitpost.2015.08.015.

McCrum, C., Epro, G., Meijer, K., Zijlstra, W., Brüggemann, G.-P., and Karamanidis, K. (2016). Locomotor stability and adaptation during perturbed walking across the adult female lifespan. *J Biomech* 49, 1244-1247. doi: 10.1016/j.jbiomech.2016.02.051.

Melzer, I., Kurz, I., Shahar, D., and Oddsson, L. I. E. (2010). Do voluntary step reactions in dual task conditions have an added value over single task for fall prediction? A prospective study. *Aging Clin Exp Res* 22, 360-366.

Mickle, K. J., Munro, B. J., Lord, S. R., Menz, H. B., and Steele, J. R. (2009). ISB Clinical Biomechanics Award 2009. Toe weakness and deformity increase the risk of falls in older people. *Clinical Biomechanics* 24, 787-791. doi: 10.1016/j.clinbiomech.2009.08.011.
Mille, M.-L., Johnson-Hilliard, M., Martinez, K. M., Zhang, Y., Edwards, B. J., and Rogers, M. W. (2013). One Step, Two Steps, Three Steps More ... Directional Vulnerability to Falls in Community-Dwelling Older People. *J Gerontol A Biol Sci Med Sci* 68, 1540–1548.

Montesinos, L., Castaldo, R., and Pecchia, L. (2018). Wearable inertial sensors for fall risk assessment and prediction in older adults: A systematic review and meta-analysis. *IEEE Transactions on Neural Systems and Rehabilitation Engineering*. doi: 10.1109/TNSRE.2017.2771383.

Mueller, M., D'Addario, M., Egger, M., Cevallos, M., Dekkers, O., Mugglin, C., et al. (2018). Methods to systematically review and meta-analyse observational studies: A systematic scoping review of recommendations. *BMC Medical Research Methodology* 18, 1–18. doi: 10.1186/s12874-018-0495-9.

Nardone, A., Siliotto, R., Grasso, M., and Schieppati, M. (1995). Influence of aging on leg muscle reflex responses to stance perturbation. *Archives of Physical Medicine and Rehabilitation* 76, 158–165. doi: 10.1016/S0003-9993(95)80025-5.

Oh-Park, M., Perera, S., and Verghese, J. (2012). Clinically meaningful change in stair negotiation performance in older adults. *Gait and Posture* 36, 532–536. doi: 10.1016/j.gaitpost.2012.05.015.

Oh-Park, M., Wang, C., and Verghese, J. (2011). Stair negotiation time in community-dwelling older adults: Normative values and association with functional decline. *Archives of Physical Medicine and Rehabilitation* 92, 2006–2011. doi: 10.1016/j.apmr.2011.07.193.

Pan, H. F., Hsu, H. C., Chang, W. N., Renn, J. H., and Wu, H. W. (2016). Strategies for obstacle crossing in older adults with high and low risk of falling. *Journal of Physical Therapy Science* 28, 1614–1620. doi: 10.1589/jpts.28.1614.

Park, S. Y., and Lee, Y. S. (2012). Kinematics of the lower limbs during obstacle crossings performed by young adults and the elderly. *Journal of Physical Therapy Science* 24, 941–944. doi: 10.1589/jpts.24.941.

Peel, N. M. (2011). Epidemiology of falls in older age. *Can J Aging* 30, 7–19. doi: 10.1017/S071498081000070X.

Pieruccini-Faria, F., and Montero-Odasso, M. (2019). Obstacle Negotiation, Gait Variability, and Risk of Falling: Results From the “Gait and Brain Study.” *J Gerontol A Biol Sci Med Sci* 74, 1422–1428. doi: 10.1093/gerona/gly254.

Pijnappels, M., Bobbert, M. F., and van Dieën, J. H. (2005). Push-off reactions in recovery after tripping discriminate young subjects, older non-fallers and older fallers. *Gait Posture* 21, 388–394. doi: 10.1016/j.gaitpost.2004.04.009.

Pijnappels, M., Delbaere, K., Sturnieks, D. L., and Lord, S. R. (2010). The association between choice stepping reaction time and falls in older adults: a path analysis model. *AGE AND AGEING* 39, 99–104.
Rehman, R. Z. U., Zhou, Y., Din, S. D., Alcock, L., Hansen, C., Guan, Y., et al. (2020). Gait analysis with wearables can accurately classify fallers from non-fallers: A step toward better management of neurological disorders. *Sensors (Switzerland)* 20, 1–17. doi: 10.3390/s20236992.

Riley, R. D., Moons, K. G. M., Snell, K. I. E., Ensor, J., Hooft, L., Altman, D. G., et al. (2019). A guide to systematic review and meta-analysis of prognostic factor studies. *BMJ (Online)* 364. doi: 10.1136/bmj.k4597.

Rum, L., Vannozzi, G., Macaluso, A., and Laudani, L. (2021). Neuromechanical response of the upper body to unexpected perturbations during gait initiation in young and older adults. *Aging Clinical and Experimental Research* 33, 909–919. doi: 10.1007/s40520-020-01592-2.

Sartini, M., Cristina, M. L., Spagnolo, A. M., Cremonesi, P., Costaguta, C., Monacelli, F., et al. (2010). The epidemiology of domestic injurious falls in a community dwelling elderly population: An outgrowing economic burden. *European Journal of Public Health* 20, 604–606. doi: 10.1093/eurpub/ckp165.

Silva, J., Madureira, J., Tonelo, C., Baltazar, D., Silva, C., Martins, A., et al. (2017). Comparing machine learning approaches for fall risk assessment. *BIOSIGNALS 2017 - 10th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017* 4, 223–230. doi: 10.5220/0006227802230230.

Smith, E., Cusack, T., Cunningham, C., and Blake, C. (2017). The Influence of a Cognitive Dual Task on the Gait Parameters of Healthy Older Adults: A Systematic Review and Meta-Analysis. *J Aging Phys Act* 25, 671–686. doi: 10.1123/japa.2016-0265.

Speechley, M., and Tinetti, M. (1991). Falls and Injuries in Frail and Vigorous Community Elderly Persons. *Journal of the American Geriatrics Society* 39, 46–52. doi: 10.1111/j.1532-5415.1991.tb05905.x.

Startzell, J. K. M. S., Owens, D. A. P., Mulfinger, L. M. P., and Cavanagh, P. R. P. (2000). Stair Negotiation in Older People: A Review. [Miscellaneous Article]. *American Geriatrics Society* 48, 267–580.

Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., et al. (2016). ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. *BMJ (Online)* 355, 1–7. doi: 10.1136/bmj.i4919.

Studenski, S., Duncan, P. W., and Chandler, J. (1991). Postural responses and effector factors in persons with unexplained falls: results and methodologic issues. *J Am Geriatr Soc* 39, 229–234.

Sun, R., Cui, C., and Shea, J. B. (2017). Aging effect on step adjustments and stability control in visually perturbed gait initiation. *Gait and Posture* 58, 268–273. doi: 10.1016/j.gaitpost.2017.08.013.

Swanenburg, J., de Bruin, E. D., Uebelhart, D., and Mulder, T. (2010). Falls prediction in elderly people: A 1-year prospective study. *Gait and Posture* 31, 317–321. doi: 10.1016/j.gaitpost.2009.11.013.

Tinetti, M. E., Speechley, M., and Ginter, S. F. (1988). Risk Factors for Falls among Elderly Persons Living in the Community. *New England Journal of Medicine* 319, 1701–1707. doi: 10.1056/nejm198812293192604.
Tokuno, C. D., Cresswell, A. G., Thorstensson, A., and Carpenter, M. G. (2010). Age-related changes in postural responses revealed by support-surface translations with a long acceleration-deceleration interval. *Clinical Neurophysiology* 121, 109-117. doi: 10.1016/j.clinph.2009.09.025.

Tunca, C., Salur, G., and Ersoy, C. (2020). Deep Learning for Fall Risk Assessment with Inertial Sensors: Utilizing Domain Knowledge in Spatiooral Gait Parameters. *IEEE Journal of Biomedical and Health Informatics* 24, 1994-2005. doi: 10.1109/JBHI.2019.2958879.

Uemura, K., Yamada, M., Nagai, K., and Ichihashi, N. (2011). Older adults at high risk of falling need more time for anticipatory postural adjustment in the precrossing phase of obstacle negotiation. *Journals of Gerontology - Series A Biological Sciences and Medical Sciences* 66 A, 904-909. doi: 10.1093/gerona/glr081.

Van Schooten, K. S., Pijnappels, M., Rispens, S. M., Elders, P. J. M., Lips, P., Daffertshofer, A., et al. (2016). Daily-life gait quality as predictor of falls in older people: A 1-year prospective cohort study. *PLoS ONE* 11, 1-13. doi: 10.1371/journal.pone.0158623.

van Schooten, K. S., Pijnappels, M., Rispens, S. M., Elders, P. J. M., Lips, P., and van Dieën, J. H. (2015). Ambulatory Fall-Risk Assessment: Amount and Quality of Daily-Life Gait Predict Falls in Older Adults. *The Journals of Gerontology: Series A* 70, 608-615. doi: 10.1093/gerona/glu225.

Woollacott, M. H., Shumway-Cook, A., and Nashner, L. M. (1986). Aging and posture control: changes in sensory organization and muscular coordination. *Int J Aging Hum Dev* 23, 97-114.

Zietz, D., Johannsen, L., and Hollands, M. (2011). Stepping characteristics and Centre of Mass control during stair descent: Effects of age, fall risk and visual factors. *Gait Posture* 34, 279-284. doi: 10.1016/j.gaitpost.2011.05.017.
**Table 1. Description of the risk studies, ordered by gait task.**

Abbreviations: years old: y; male: m; female: f.

Shading: not reported

Risk of bias: Low, Moderate, Serious

Risk assessment:

* Prospective/Retrospective: comparison of older adults who did and did not fall during a period following/preceding the measurement

Assessment of participant status:

* Prospective fall history: ✓ if the subjects were prospectively followed-up after the measurement to determine whether they fall

* Retrospective fall history: ✓ if the subjects were asked whether they had fallen in the months preceding the measurement

* Physical/mental level:

* ✓ if physical/mental level was tested

* x if physical/mental level was tested and subjects with low fitness were excluded from the study.

Medicine usage

* ✓ if participants took medication during the measurement period

* x if participants did not take medication during the measurement period

Measurement devices: if force platforms or infrared cameras were used, but their number was not reported, this is indicated by ✓

Task description: Expected:

* x if unexpected changes in the task occurred from trial to trial (for example: if various obstacles or stairs were used and these were presented in random order, or if perturbations occurred at random times)

* ✓ otherwise

<table><thead><tr><th rowspan="2">Article</th><th rowspan="2">Risk of bias</th><th rowspan="2">Risk assessment</th><th colspan="4">Assessment of participant status</th><th colspan="6">Participants</th><th colspan="2">Measurement devices</th><th colspan="4">Task description</th></tr><tr><th>Prospective fall history</th><th>Retrospective fall history</th><th>Physical level</th><th>Mental level</th><th>Community dwelling</th><th>Medicine usage</th><th>Younger adults</th><th>Older adults at higher risk</th><th>Older adults at lower risk</th><th>Force platform</th><th>Infrared cameras system</th><th>Task</th><th>Expected</th><th>Self-selected walking velocity</th><th>Randomization or fatigue avoidance</th></tr></thead><tbody><tr><td colspan="19" style="background-color: #FFDAB9; font-weight: bold;">Stairs</td></tr><tr><td>Ackermans (2019)</td><td>●</td><td>Retrospective</td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>25 (24.5 ± 3.3 y)<br/>No gender information</td><td>27 (71.1 ± 4.1 y)<br/>No gender information</td><td>43 (71.1 ± 4.1 y)<br/>No gender information</td><td>4</td><td>24</td><td>Descent 6 steps, each:<br/>Height: 20 cm<br/>Length: 25 cm</td><td>✓</td><td>Self-selected</td><td>✓</td></tr><tr><td>Ackermans (2021)</td><td>●</td><td>Prospective</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>61</td><td>21</td><td>4</td><td>24</td><td>Descent + ascent 7 steps, each:</td><td>✓</td><td>Self-selected</td><td>✓</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td>Berg Balance Scale<br/>Fullerton Functional Fitness Test</td><td>Activities-specific Balance Confidence Scale<br/>Falls Efficacy Scale-International Montreal Cognitive Assessment<br/>Nottingham extended Activities of Daily Living FRAT<br/>Freiburg Vision Test</td><td></td><td></td><td>(72.1 ± 5.2y)<br/>(28 m & 53f in total)</td><td>(72.1 ± 5.2y)<br/>(28 m & 53f in total)</td><td></td><td>Height: 20 cm<br/>Length: 25 cm</td><td></td><td></td></tr><tr><td>Brodowski (2021)</td><td>●</td><td>Health status (hospitalized versus healthy)</td><td>✓</td><td>x<br/>Timed-Up-and-Go</td><td>x<br/>Mini mental status &lt; 24</td><td>hospitalized versus healthy</td><td></td><td>57<br/>(77.3 ± 7.8y)<br/>(26 m & 31f)</td><td>56<br/>(70.3 ± 5.3y)<br/>(17 m & 39f)</td><td></td><td>Descent + ascent<br/>13 steps, each:<br/>Height: 16 cm<br/>Length: 30 cm</td><td>✓</td><td>Self-selected</td><td>✓</td></tr><tr><td>Carli (2014)</td><td>●</td><td>Physical level</td><td></td><td>✓<br/>Stair stepping test<br/>Timed-Up-and-Go<br/>Five Times Sit-to-Stand test</td><td></td><td>✓</td><td></td><td>13<br/>(72.61 ± 0.28 y)<br/>(4 m & 30f in total)</td><td>21<br/>(69.17 ± 4.96 y)<br/>(4 m & 30f in total)</td><td>✓</td><td>Descent + ascent<br/>4 steps, each:<br/>Height: 17.5 cm<br/>Length: 29 cm</td><td>✓</td><td>Self-selected</td><td>✓</td></tr><tr><td>Oh-Park (2012)</td><td>●</td><td>Functional decline over 1 year</td><td>✓</td><td>✓<br/>Short physical performance battery<br/>Activities of Daily Living<br/>Self-reported walking ability<br/>Illness index summary score<br/>Disability score<br/>Self-reported stair negotiation</td><td>✓<br/>Blessed test<br/>Depression score<br/>Fear of falling</td><td>✓</td><td>x</td><td>113<br/>(80.5 ± 4.4 y)<br/>(45 m & 68 f)</td><td>197<br/>(80.1 ± 65.1 y)<br/>(80 m & 117 f)</td><td></td><td>Descent + ascent<br/>3 steps, each:<br/>Height: 18 cm<br/>Length: 26 cm</td><td>✓</td><td>Self-selected</td><td>✓</td></tr><tr><td>Oh-Park (2011)</td><td>●</td><td>Retrospective</td><td>✓</td><td>✓<br/>Blessed test</td><td></td><td>✓</td><td>x</td><td>245</td><td>268</td><td></td><td>Descent + ascent</td><td>✓</td><td>Self-selected</td><td>✓</td></tr></table>
<table><tbody><tr><td></td><td></td><td></td><td>Activities of Daily Living<br/>Self-reported<br/>stair negotiation<br/>Illness index<br/>summary score<br/>Disability score</td><td>Depression score<br/>Fear of falling</td><td></td><td></td><td>(In total:<br/>80.8 ± 5.1<br/>y)<br/>(204 m &<br/>309 f)</td><td>(In total:<br/>80.8 ± 5.1<br/>y)<br/>(204 m &<br/>309 f)</td><td></td><td>3 steps, each:<br/>Height: 18 cm<br/>Length: 26 cm</td><td></td><td></td><td></td></tr><tr><td>Zietz (2011)</td><td>●</td><td>Physical and<br/>mental level</td><td>✓</td><td>✓<br/>Visual acuity<br/>Berg Balance<br/>Scale</td><td>✓<br/>Modified Falls Efficacy<br/>Scale<br/>Stair Self-Efficacy<br/>Questionnaire</td><td>✓</td><td>✓</td><td>8<br/>(26 ± 4 y)<br/>(1 m & 7 f)</td><td>7<br/>(79.3 ± 6.4<br/>y)<br/>(1 m & 6 f)</td><td>7<br/>(72.1 ± 3.8<br/>y)<br/>(1 m & 6 f)</td><td>13</td><td>Descent<br/>5 steps, each:<br/>Height: 17 cm<br/>Length: 27 cm</td><td>✓</td><td>Self-<br/>selected</td><td>✓</td></tr><tr><td colspan="16">Perturbations</td></tr><tr><td>Gerards<br/>(2021)</td><td>●</td><td>Retrospective</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>8<br/>(70.6 ± 3.6<br/>y)<br/>(4 m & 4 f)</td><td>12<br/>(70 ± 2.1 y)<br/>(6 m & 6 f)</td><td></td><td>12</td><td>Forward support<br/>acceleration at 3<br/>m/s²</td><td>×</td><td>Fixed<br/>velocity<br/>0.4 m/s,<br/>0.8 m/s,<br/>1.6 m/s</td><td></td></tr><tr><td>Pijnappels<br/>(2005)</td><td>●</td><td>Balance<br/>performance</td><td></td><td></td><td></td><td></td><td></td><td>12<br/>(27.1 ± 4.3<br/>y)<br/>(6 m & 6 f)</td><td>7<br/>(67.9 ± 2.7<br/>y)<br/>(1 m & 6 f)</td><td>4<br/>(67.6 ± 2.6<br/>y)<br/>(3 m & 1 f)</td><td>✓</td><td>✓</td><td>Unexpected<br/>appearance of a<br/>15 cm high<br/>obstacle which<br/>trips the swing<br/>leg</td><td>×</td><td>Self-<br/>selected</td><td></td></tr><tr><td colspan="16">Obstacles</td></tr><tr><td>Brach<br/>(2011)</td><td>●</td><td>Walking decline<br/>over 1 year</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>18<br/>(76.2 ± 5.9<br/>y)<br/>(3 m & 17 f)</td><td>53<br/>(75.9 ± 5.9<br/>y)<br/>(17 m & 70<br/>f)</td><td></td><td></td><td>Height: 6 cm<br/>(positioned<br/>2 m from the<br/>starting line), 30<br/>cm (positioned 4<br/>m from the<br/>starting line)</td><td>✓</td><td>Self-<br/>selected</td><td></td></tr><tr><td>Chou (2003)</td><td>●</td><td>Balance<br/>complaints &<br/>fall history</td><td>✓</td><td>✓<br/>Sensory<br/>organization<br/>test</td><td>×</td><td>Folstein Mini mental<br/>status &lt; 24</td><td>✓</td><td>9<br/>(72 ± 6.4 y)<br/>(7 m & 2 f)</td><td>6<br/>(76 ± 3.9 y)<br/>(1 m & 5 f)</td><td>6</td><td></td><td>Height: 20 % of<br/>leg length<br/>Length: 20 cm<br/>Width: 80 cm</td><td>✓</td><td>Self-<br/>selected</td><td>✓</td></tr><tr><td>Hansson<br/>(2021)</td><td>●</td><td>Prospective</td><td>✓</td><td>✓<br/>Headshake<br/>test</td><td></td><td></td><td>✓</td><td>42</td><td>59</td><td></td><td></td><td>Height: 30 cm</td><td>✓</td><td>Self-<br/>selected</td><td>✓</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td>Dix-Hallpike test<br>Head impulse test<br>Vestibular function<br>Time-Up-and-Go</td><td></td><td></td><td>(75.7 ± 6.2 y)<br>(5 m & 37 f)</td><td>(74.6 ± 5.2 y)<br>(5 m & 54 f)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Guadagnin (2020)</td><td>●</td><td>Retrospective</td><td>✓</td><td>✓<br>Mini mental status</td><td></td><td></td><td>10 (66 ± 5 y)<br>(all females)</td><td>10 (66 ± 4 y)<br>(all females)</td><td>6</td><td></td><td>Height: 2.5, 5, 10, 15% of body height<br>Length: 2.5 cm<br>Width: 200 cm</td><td>✓</td><td>Self-selected</td></tr><tr><td>Pan (2016)</td><td>●</td><td>Physical level</td><td></td><td>✓<br>Tinetti Balance Gait subscale of the POMA Low</td><td></td><td>✓</td><td>10 (62.5 ± 6.6 y)<br>No gender information</td><td>10 (65.6 ± 8.7 y)<br>No gender information</td><td>✓</td><td></td><td>Height: 10%, 20% and 30% of leg length</td><td>✓</td><td>Self-selected</td></tr><tr><td>Pieruccini-Faria (2019)</td><td>●</td><td>Retrospective</td><td>✓</td><td>✓<br>Trail Making Test<br>Physical Activity Scale<br>Short Physical Performance Battery</td><td>✓<br>Rey Auditory Verbal Learning<br>Montreal Cognitive Assessment<br>Fear of falling</td><td>✓</td><td>27 (72.2 ± 5.1 y)<br>No gender information</td><td>110 (72.2 ± 5.1 y)<br>No gender information</td><td>✓</td><td></td><td>Height: 30 cm<br>Length: 3 cm<br>Width: 70 cm</td><td>✓</td><td>Self-selected</td></tr><tr><td>Uemura (2011)</td><td>●</td><td>Retrospective</td><td>✓</td><td>✓<br>10 m walking test</td><td>✓<br>Rapid Dementia Screening test<br>Fall Efficacy Scale</td><td>✓</td><td>26 (81.6 ± 7.3 y)<br>(9 m & 17 f)</td><td>50 (79.7 ± 6.9 y)<br>(17 m & 33 f)</td><td>✓</td><td></td><td>Height: 2.4 cm<br>Length: 1 cm<br>Width: 91 cm</td><td>✓</td><td>Self-selected</td></tr></table>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>
<caption>**Table 2:** Significant and non-significant findings in risk studies</caption>

Overview of the reported outcome parameters and corresponding articles reporting either significant or non-significant findings. **Risk studies** (comparing older adults with higher and lower risk) are in bold, and underlined if fall risk is assessed **prospectively**, or in italics if risk is evaluated based on **physical or mental level**. Ageing studies (comparing younger and older adults) are not in bold. Studies with a serious risk of bias are in red. Studies for which the significance level was not reported, but for which the sample size was sufficient (see G\*method calculation in the Methods) are indicated with an asterisk (*). Abbreviations: Sign. significant; N.S. non-significant.

<table><thead><tr><th rowspan="2">Temporal outcomes</th><th colspan="2">Total</th><th colspan="2">Stairs</th><th colspan="2">Perturbations</th><th colspan="2">Obstacles</th></tr><tr><th>Sign.</th><th>N.S.</th><th>Sign.</th><th>N.S.</th><th>Sign.</th><th>N.S.</th><th>Sign.</th><th>N.S.</th></tr></thead><tbody><tr><td rowspan="5">Stance, swing and compensatory duration or percentage of stance phase</td><td>1</td><td>5</td><td rowspan="5"><b>Carli (2014)</b><br>Begg (2000)<br>Isue (2009)<br>Isue (2014)<br>Larsen (2008)<br>Novak (2011)</td><td rowspan="5">Crosbie (2003)<br>Dewolf (2021)</td><td rowspan="5">Martelli (2017)<br>Shulman (2018)<br>Shulman (2019)</td><td rowspan="5"><b>Gerards (2021)</b><br><b>Pijnappels (2005)</b><br>Laudani (2021)<br>Martelli (2017)<br><b>Nachmani (2020)</b><br>Ren (2022)<br>Roeles (2018)</td><td rowspan="5">Caetano (2016)<br>Chien (2018)<br>Kim (2013)<br>Park (2012)<br>Uchiyama (2012)</td><td rowspan="5"><b>Chou (2003)</b><br><b>Pan (2016)</b><br><b>Pieruccini (2019)</b><br>Chen (1994)<br>Hahn (2004)<br>Lowrey (2007)</td></tr><tr><td>13</td><td>10</td></tr><tr><td></td><td></td></tr><tr><td></td><td></td></tr><tr><td></td><td></td></tr><tr><td>Step time variability</td><td>1</td><td>0</td><td></td><td></td><td></td><td></td><td><b>Pieruccini (2019)</b></td><td></td></tr><tr><td>Duration of anticipatory postural adjustments</td><td>1</td><td>0</td><td></td><td></td><td></td><td></td><td><b>Uemura (2011)</b></td><td></td></tr><tr><td rowspan="2">Walking, approaching, or crossing speed</td><td>2</td><td>9</td><td><b>Brodowski (2021)*</b><br><b>Oh-Park (2012)*</b></td><td><b>Ackermans (2019)</b><br><b>Ackermans (2021)</b><br><b>Oh-Park (2011)</b></td><td></td><td><b>Pijnappels (2005)</b></td><td></td><td><b>Brach (2011)</b><br><b>Chou (2003)</b><br><b>Guadagnin (2020)</b><br><b>Pan (2016)</b><br><b>Pieruccini (2019)</b><br>Chen (1994)<br>Hahn (2004)</td></tr><tr><td>14</td><td>6</td><td></td><td></td><td>Shulman (2019)</td><td>Laudani (2021)</td><td>Caetano (2016)<br>Chen (1991)<br>Eyal (2020)<br>Lowrey (2007)<br>McFadyen (2002)<br>Park (2012)</td><td></td></tr><tr><td>Walking speed variance</td><td>0</td><td>2</td><td></td><td></td><td><b>Ackermans (2019)</b><br><b>Ackermans (2021)</b></td><td></td><td></td><td></td></tr><tr><td>Decrease in walking speed relative to steady walking</td><td>1</td><td>0</td><td></td><td></td><td></td><td></td><td><b>Brach (2011)</b></td><td></td></tr><tr><th rowspan="2">Foot and step outcomes</th><th colspan="2">Total</th><th colspan="2">Stairs</th><th colspan="2">Perturbations</th><th colspan="2">Obstacles</th></tr><tr><th>Sign.</th><th>N.S.</th><th>Sign.</th><th>N.S.</th><th>Sign.</th><th>N.S.</th><th>Sign.</th><th>N.S.</th></tr></tbody></table>
<table><tbody><tr><td>Step length</td><td>0<br>22</td><td>7<br>9</td><td>Begg (2000)<br>Chiu (2015)<br>Crosbie (2003)</td><td>Zietz (2011)<br>Huang (2008)</td><td>Afschrift (2019)<br>Debelle (2021)<br>Laudani (2021)<br>Martelli (2017)<br>McIntosh (2016)<br>Nachmani (2020)<br>Shulman (2018)<br>Shulman (2019)<br>Qiao (2018)</td><td>Gerards (2021)<br>Pijnappels (2005)<br>Ren (2022)<br>Roeles (2018)<br>Shulman (2019)</td><td>Kulkarni (2021)<br>Caetano (2016)<br>Chen (1991)<br>Chien (2018)<br>Eyal (2020)<br>Lowrey (2007)<br>McFadyen (2002)<br>Park (2012)<br>Weerdesteyn (2005a)<br>Weerdesteyn (2005b)</td><td>Chou (2003)<br>Guadagnin (2020)<br>Pan (2016)<br>Pieruccini (2019)<br>Chen (1994)<br>Hahn (2004)<br>Lu (2006)<br>Luo (2022)<br>Mckenzie (2004)</td></tr><tr><td>Step length variability</td><td>1<br>1</td><td>1<br>1</td><td></td><td>Zietz (2011)</td><td>Qiao (2018)</td><td></td><td>Pieruccini (2019)</td><td>Luo (2022)</td></tr><tr><td>Step width</td><td>1<br>6</td><td>3<br>9</td><td></td><td>Zietz (2011)<br>Chiu (2015)</td><td>Afschrift (2019)<br>McIntosh (2016)<br>Shulman (2018)<br>Shulman (2019)</td><td>Gerards (2021)<br>Kazanski (2020)<br>Laudani (2021)<br>Ren (2022)<br>Roeles (2018)<br>Qiao (2018)</td><td>Pan (2016)<br>Chen (1991)<br>Lowrey (2007)</td><td>Chou (2003)<br>Hahn (2004)<br>Luo (2022)<br>Park (2012)</td></tr><tr><td>Step width variability</td><td>0<br>3</td><td>1<br>1</td><td></td><td>Zietz (2011)</td><td>Kazanski (2020)<br>Qiao (2018)</td><td></td><td>Chen (1991)</td><td>Luo (2022)</td></tr><tr><td>Step symmetry</td><td>1<br>1</td><td>0<br>0</td><td></td><td></td><td></td><td></td><td>Pan (2016)<br>Kulkarni (2021)</td><td></td></tr><tr><td>Foot clearance to object<br>(when the foot is in the air)</td><td>2<br>7</td><td>4<br>6</td><td>Begg (2000)<br>Francksen (2020)</td><td>Zietz (2011)<br>Ackermans (2019)<br>Ackermans (2021)<br>Francksen (2022)</td><td></td><td></td><td>Pan (2016)<br>Chien (2018)<br>Luo (2022)<br>Lu (2006)<br>Maidan (2018)<br>Weerdesteyn (2005a)</td><td>Chou (2003)<br>Guadagnin (2020)<br>Chen (1991)<br>Draganich (2004)<br>LoJacono (2018)<br>Lowrey (2007)<br>Mckenzie (2004)</td></tr><tr><td>Variance of foot clearance to<br>object</td><td>0<br>2</td><td>3<br>1</td><td></td><td>Zietz (2011)<br>Ackermans (2019)<br>Ackermans (2021)</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><tbody><tr><td>Foot distance to object<br>(when the foot is on the<br>ground)</td><td>0<br>9</td><td>2<br>4</td><td>Francksen (2020)<br>Begg (2000)</td><td></td><td></td><td>McFadyen (2002)</td><td>Luo (2022)</td></tr><tr><td>Foot contact (distance to<br>edge or percentage of<br>length)</td><td>0<br>3</td><td>2<br>0</td><td>Begg (2000)<br>Francksen (2020)<br>Francksen (2022)</td><td>Ackermans (2019)<br>Ackermans (2021)</td><td></td><td>Chen (1991)<br>Chien (2018)<br>Kulkarni (2021)<br>Lowrey (2007)<br>Lu (2006)<br>Maidan (2018)<br>McFadyen (2002)<br>Weerdesteyn<br>(2005a)</td><td>Guadagnin (2020)<br>Pan (2016)<br>Chen (1994)<br>Draganich (2004)<br>Mckenzie (2004)<br>LoJacono (2018)</td></tr><tr><td>Variance of foot contact<br>(percentage of length)</td><td>0</td><td>2</td><td></td><td>Ackermans (2019)<br>Ackermans (2021)</td><td></td><td></td><td></td></tr><tr><td>Foot range of motion</td><td>0<br>1</td><td>1<br>1</td><td>Francksen (2022)</td><td>Dewolf (2021)</td><td></td><td></td><td>Chou (2003)</td></tr><tr><td>Center of Mass (CoM)<br>outcomes</td><td colspan="2">Total</td><td colspan="2">Stairs</td><td colspan="2">Perturbations</td><td colspan="2">Obstacles</td></tr><tr><td>Range of Motion (RoM) or<br>maximum RoM of CoM</td><td>1<br>4</td><td>0<br>2</td><td>Sign.<br>Hsue (2014)</td><td>N.S.<br>Mian (2007)</td><td>Sign.<br>McIntosh (2016)</td><td>N.S.<br>McIntosh (2016)</td><td>Sign.<br>Chou (2003)<br>Hahn (2004)<br>Lowrey (2007)<br>Wang (2010)</td><td>N.S.<br>Chou (2003)<br>Hahn (2004)<br>Lowrey (2007)<br>Wang (2010)</td></tr><tr><td>CoM velocity</td><td>1<br>6</td><td>0<br>5</td><td>Bosse (2012)<br>Dixon (2018)<br>Foster (2019)<br>Hsue (2014)</td><td>Mian (2007)<br>Novak (2016)</td><td>McIntosh (2016)<br>Shulman (2018)<br>Shulman (2019)</td><td>McIntosh (2016)<br>Shulman (2018)<br>Shulman (2019)</td><td>Chou (2003)<br>Mckenzie (2004)<br>Kim (2013)</td><td>Hahn (2004)<br>Wang (2010)<br>Kim (2013)</td></tr><tr><td>CoM acceleration and<br>variance</td><td>0<br>2</td><td>1<br>0</td><td>Foster (2019)</td><td>Zietz (2011)</td><td></td><td></td><td>Wang (2010)</td><td></td></tr><tr><td>Force plate outcomes</td><td colspan="2">Total</td><td colspan="2">Stairs</td><td colspan="2">Perturbations</td><td colspan="2">Obstacles</td></tr><tr><td>Vertical ground reaction<br>force or shear force</td><td>1<br>6</td><td>0<br>2</td><td>Sign.<br>Carli (2014)<br>Christina (2002)<br>Hamel (2005)<br>Larsen (2008)</td><td>N.S.<br>Christina (2002)<br>Hamel (2005)<br>Larsen (2008)</td><td>Sign.<br>Laudani (2021)</td><td>N.S.<br>Debelle (2021)<br>Rum (2020)</td><td>Sign.<br>Kim (2013)</td><td>N.S.<br>Kim (2013)</td></tr></tbody></table>
<table><tbody><tr><td></td><td></td><td></td><td>Reeves (2009)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Loading rate</td><td>1<br>3</td><td>0<br>0</td><td><b>Carli (2014)</b><br>Christina (2002)<br>Hamel (2005)<br>Larsen (2008)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Required coefficient of<br>friction</td><td>0<br>1</td><td>2<br>1</td><td><b>Ackermans (2021)</b><br>Ackermans (2019)<br>Christina (2002)<br>Hamel (2005)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Vertical or braking impulse</td><td>1<br>0</td><td>0<br>1</td><td><b>Carli (2014)</b></td><td></td><td></td><td>Laudani (2021)</td><td></td><td></td></tr><tr><td><b>Dynamic stability<br>outcomes</b></td><td><b>Total</b></td><td></td><td><b>Stairs</b></td><td><b>Perturbations</b></td><td><b>Obstacles</b></td><td></td><td></td><td></td></tr><tr><td></td><td>Sign.</td><td>N.S.</td><td>Sign.</td><td>Sign.</td><td>Sign.</td><td>Sign.</td><td>Sign.</td><td>Sign.</td></tr><tr><td>Margin of stability</td><td>0<br>8</td><td>4<br>3</td><td><b>Ackermans (2019)</b><br><b>Ackermans (2021)</b><br><b>Zietz (2011)</b><br>Bosse (2012)<br>Novak (2016)</td><td><b>Gerards (2021)</b><br>Bosquée (2021)<br>Jeon (2022b)<br>Laudani (2021)<br>Martelli (2017)<br>McCrum (2016)<br><b>Nachmani (2020)</b></td><td></td><td></td><td></td><td></td></tr><tr><td>Angular momentum</td><td>1</td><td>0</td><td></td><td><b>Pijnappels (2005)</b></td><td></td><td></td><td></td><td></td></tr><tr><td><b>Joint and segment<br>outcomes</b></td><td><b>Total</b></td><td></td><td><b>Stairs</b></td><td><b>Perturbations</b></td><td><b>Obstacles</b></td><td></td><td></td><td></td></tr><tr><td>Ankle</td><td>joint moment</td><td>0<br>9</td><td>1<br>2</td><td>Foster (2019)<br>Isue (2009)<br>Novak (2011)<br>Reeves (2009)</td><td>Debelle (2021)<br>Liu (2009)<br>Yoo (2021)</td><td><b>Pijnappels (2005)</b><br>Laudani (2021)<br>Ren (2022)</td><td>Draganich (2004)<br>McFadyen (2002)</td><td></td></tr><tr><td>Knee</td><td>joint moment</td><td>0<br>7</td><td>1<br>4</td><td>Isue (2009)<br>Novak (2011)<br>Reeves (2009)</td><td>Foster (2019)<br>Liu (2009)<br>Yoo (2021)</td><td><b>Pijnappels (2005)</b><br>Debelle (2021)<br>Ren (2022)</td><td>Draganich (2004)<br>Wang (2010)</td><td>McFadyen (2002)</td></tr><tr><td>Hip</td><td>motion</td><td>0<br>5</td><td>1<br>2</td><td>Isue (2009)</td><td></td><td><b>Pijnappels (2005)</b><br>Chien (2018)<br>Lu (2006)<br>McFadyen (2002)<br>Park (2012)</td><td>Chien (2018)<br>Lu (2006)<br>McFadyen (2002)<br>Chen (1991)<br>Draganich (2004)</td><td></td></tr><tr><td></td><td>joint moment</td><td>0</td><td>1</td><td></td><td></td><td><b>Pijnappels (2005)</b></td><td></td><td></td></tr></tbody></table>
<table><tr><td></td><td></td><td>8</td><td>0</td><td>Novak (2011)</td><td></td><td>Debelle (2021)<br/>Liu (2009)<br/>Ren (2022)<br/>Yoo (2021)</td><td></td><td>Draganich (2004)<br/>McFadyen (2002)<br/>Wang (2010)</td><td></td></tr></table>
<figure><img src="image_7.png" /><figcaption>Figure 1: Overview of the systematic article extraction</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 2: Forest plot of walking speed. Circles: open blue—younger adults, yellow closed—older adults with low fall risk, red closed—older adults with high fall risk, brown closed—older adults with unspecified fall risk. For Brach (2011): older adults whose gait speed improved / stayed the same / deteriorated after a year are indicated in green / yellow / red.; m/s, meters per second; N.A.*, not applicable, the standard deviation was not given; n.s., not significant. Ntot is the total number of participants.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 3: Foot clearance definitions from different studies. V, vertical; H, horizontal.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure 4: Forest plot of heel/foot/toe clearance. Circles: open blue—younger adults, yellow closed—older adults with low fall risk or non-fallers, red closed—older adults with high fall risk or fallers. Ntot is the total number of participants. n.s. is non-significant finding</figcaption></figure>