Poor mobility and lower limb weakness are associated with three distinct depressive symptom trajectories over 6 years in older people.

OBJECTIVES: Physical decline can be associated with the onset of depressive symptoms in later life. This study aimed to identify physical and lifestyle risk factors for depressive symptom trajectories in community-dwelling older adults. METHODS: Participants were 553 people aged 70-90 years who underwent baseline physical, psychological and lifestyle assessments. Group-based trajectory analysis was used to identify patterns of depressive symptom development over 6 years of follow-up. Strengths of associations between baseline functional test performances and depressive symptom trajectories were evaluated with univariable ordinal models. Subsequently, the adjusted cumulative odds ratio for the association between identified risk factors, demographic factors and baseline anti-depressant use were measured using multivariable ordinal logistic regression. RESULTS: Three distinct depressive symptom trajectories were identified: a low-and-stable course (10% of participants), a low-and-increasing course (81%) and a moderate-and-increasing course (9%). Timed Up and Go test time was the strongest risk factor of depressive symptom trajectory, followed by Five Times Sit-to-Stand test performance, planned physical activity levels, and knee extension strength (adjusted standardised ORs 1.65, 95% CI 1.34-2.04; 1.44, 95% CI 1.16-1.77; 1.44, 95% CI 1.17-1.76 and 1.41, 95% CI 1.15-1.73 respectively). After adjusting for age, sex, body mass index and baseline anti-depressant use, Timed Up and Go test performance and knee extension strength were independently and significantly associated with depressive trajectories. CONCLUSIONS: Timed Up and Go test times, Five Times Sit-to-Stand test performance, planned physical activity levels and knee extension strength are associated with three discrete depressive symptom trajectories. These clinical tests may help identify older adults aged 70-90 years at risk of developing depressive symptoms and help guide subsequent strength and mobility interventions.

Is Maximal or Usual Walking Speed from Large Scale Wrist Sensor Data Better at Predicting Dementia, Depression and Death?

Older people are at increased risk of many adverse health outcomes, including dementia and depression, that burden the global health system. This paper presents algorithms for the large-scale assessment of daily walking speeds. We hypothesize that (i) data from wrist-worn sensors can be used to assess walking speed accurately; and that (ii) maximal daily walking speed is a better predictor of health outcomes than usual daily walking speed. First, algorithms were developed and tested using data from 101 participants aged 19 to 91 (47 ± 18) years. Participants wore an AX3 accelerometer (Axivity, UK) on their dominant wrist while undertaking daily life activities with electronic walkway data used for ground truth. Subsequently, prediction models for dementia, depression and death were developed using the data of 47,406 participants (≥ 60 years) from the UK Biobank study. Daily walking speeds were derived from 7-day AX3 data with time-to-events using electronic health records. The accuracy of derived walking speeds was assessed using root mean square error (RMSE). Time-to-events were modelled using Cox regression with inverse hazard ratios reported for univariable models and Harrell's concordance for multivariable models. Derived walking speeds had an RMSE of between 3% and 4% depending on arm position. We found that for simple models, maximal walking speed was significantly better than usual walking speed at predicting time to dementia (1.62 vs 1.34), depression (1.29 vs 1.17) and death (1.56 vs 1.27). However, the addition of known risk factors in subsequent multivariable models reduced the apparent benefit of using maximal as opposed to usual daily walking speed as the gait parameter. In summary, walking speed was accurately measured with a wrist-worn device, and maximal daily waking speed may be better than usual daily walking speed at predicting some adverse health outcomes.Clinical Relevance- This study demonstrated the validity of using a simple and unobtrusive wrist-worn sensor to remotely assess daily walking speed. As a single, modifiable and easily understood measure, maximal walking speed was shown to be better than usual walking speed at predicting time-to-dementia, depression and death. Therefore, the inclusion of maximal daily walking speed into screening programs and clinical interventions presents a promising area for further research.

Prediction of injurious falls in older adults using digital gait biomarkers extracted from large-scale wrist sensor data.

OBJECTIVES: To determine whether digital gait biomarkers captured by a wrist-worn device can predict injurious falls in older people and to develop a multivariable injurious fall prediction model. DESIGN: Population-based longitudinal cohort study. SETTING AND PARTICIPANTS: Community-dwelling participants of the UK Biobank study aged 65 and older (n = 32,619) in the United Kingdom. METHODS: Participants were assessed at baseline on daily-life walking speed, quality, quantity and distribution using wrist-worn accelerometers for up to 7 days. Univariable and multivariable Cox proportional hazard regression models were used to analyse the associations between these parameters and injurious falls for up to 9 years. RESULTS: Five percent of the participants (n = 1,627) experienced at least one fall requiring medical attention over a mean of 7.0 ± 1.1 years. Daily-life walking speed, gait quality, quantity of walking and distribution of daily walking were all significantly associated with the incidence of injurious falls (P < 0.05). After adjusting for sociodemographics, lifestyle factors, comorbidities, handgrip strength and reaction time; running duration, total step counts and usual walking speed were identified as independent and significant predictors of falls (P < 0.01). These associations were consistent in those without a history of previous fall injuries. In contrast, step regularity was the only risk factor for those with a previous fall history after adjusting for covariates. CONCLUSIONS: Daily-life gait speed, quantity and quality, derived from wrist-worn sensors, are significant predictors of injurious falls in older people. These digital gait biomarkers could potentially be used to identify fall risk in screening programs and integrated into fall prevention strategies.

Impact of mobile phone use on accidental falls risk in young adult pedestrians.

Background: Mobile phone use is known to be a distraction to pedestrians, increasing their likelihood of crossing into oncoming traffic or colliding with other people. However, the effect of using a mobile phone to text while walking on gait stability and accidental falls in young adults remains inconclusive. This study uses a 70 cm low friction slip hazard and the threat of hazard to investigate the effects of texting while walking on gait stability, the ability to recover balance after a slip hazard and accidental falls. Methods: Fifty healthy young adults performed six walking tasks, and one seated texting task in random order. The walks were conducted over a 10-m walkway. Four progressive hazard levels were used: 1) Seated; 2) Normal Walk (walking across the walkway with no threat of a slip); 3) Threat (walking with the threat of a slip); and 4) Slip (walking with an actual 70 cm slip hazard). The three walking conditions were repeated twice with and without the mobile phone texting dual-task. Gait kinematics and trunk posture were recorded using wearable sensors attached to the head, trunk, pelvis and feet. Study outcomes were analyzed using repeated measures analysis of variance with significance set to P≤.05. Results: Mobile phone use significantly impaired postural balance recovery when slipping, as demonstrated by increased trunk sway. Mobile phone use negatively impacted gait stability as demonstrated by increased step time variability and decreased harmonic ratios. Increased hazard levels also led to reduced texting accuracy. Conclusions: Using a mobile phone to text while walking may compete with locomotor tasks, threat assessment and postural balance control mechanisms, which leads to an increased risk of accidental falls in young adults. Pedestrians should therefore be discouraged through new educational and technology-based initiatives (for example a "texting lock" on detection of walking) from texting while walking on roadside footpaths and other environments where substantial hazards to safety exist.

Prediction of Incident Depression in Middle-Aged and Older Adults using Digital Gait Biomarkers Extracted from Large-Scale Wrist Sensor Data.

OBJECTIVES: To determine if digital gait biomarkers captured by a wrist-worn device can predict the incidence of depressive episodes in middle-age and older people. DESIGN: Longitudinal cohort study. SETTING AND PARTICIPANTS: A total of 72,359 participants recruited in the United Kingdom. METHODS: Participants were assessed at baseline on gait quantity, speed, intensity, quality, walk length distribution, and walk-related arm movement proportions using wrist-worn accelerometers for up to 7 days. Univariable and multivariable Cox proportional-hazard regression models were used to analyze the associations between these parameters and diagnosed incident depressive episodes for up to 9 years. RESULTS: A total of 1332 participants (1.8%) had incident depressive episodes over a mean of 7.4 ± 1.1 years. All gait variables, except some walk-related arm movement proportions, were significantly associated with the incidence of depressive episodes (P < .05). After adjusting for sociodemographic, lifestyle, and comorbidity covariates; daily running duration, steps per day, and step regularity were identified as independent and significant predictors (P < .001). These associations held consistent in subgroup analysis of older people and individuals with serious medical conditions. CONCLUSIONS AND IMPLICATIONS: The study findings indicate digital gait quality and quantity biomarkers derived from wrist-worn sensors are important predictors of incident depression in middle-aged and older people. These gait biomarkers may facilitate screening programs for at-risk individuals and the early implementation of preventive measures.

Development and large-scale validation of the Watch Walk wrist-worn digital gait biomarkers.

Digital gait biomarkers (including walking speed) indicate functional decline and predict hospitalization and mortality. However, waist or lower-limb devices often used are not designed for continuous life-long use. While wrist devices are ubiquitous and many large research repositories include wrist-sensor data, widely accepted and validated digital gait biomarkers derived from wrist-worn accelerometers are not available yet. Here we describe the development of advanced signal processing algorithms that extract digital gait biomarkers from wrist-worn devices and validation using 1-week data from 78,822 UK Biobank participants. Our gait biomarkers demonstrate good test-retest-reliability, strong agreement with electronic walkway measurements of gait speed and self-reported pace and significantly discriminate individuals with poor self-reported health. With the almost universal uptake of smart-watches, our algorithms offer a new approach to remotely monitor life-long population level walking speed, quality, quantity and distribution, evaluate disease progression, predict risk of adverse events and provide digital gait endpoints for clinical trials.

Short Daily-Life Walking Bouts and Poor Self-Reported Health Predict the Onset of Depression in Community-Dwelling Older People: A 2-Year Longitudinal Cohort Study.

OBJECTIVES: This study aimed to assess whether the amount and quality of daily-life walking obtained using wearable technology can predict depression onset over a 2-year period, independently of self-reported health status. DESIGN: Longitudinal cohort study. SETTING AND PARTICIPANTS: Three-hundred twenty-two community-dwelling older people recruited in Sydney, Australia. METHODS: Participants were assessed at baseline on (1) depressive symptoms using the Patient Health Questionnaire-9; (2) average weekly physical activity levels over the past month using the Incidental and Planned Activity Questionnaire, (3) clinical mobility tests (ie, short physical performance battery, timed up-and-go test, 6-m walk test); and (4) amount and quality of daily-life walking assessed with a trunk accelerometer (MoveMonitor, McRoberts) for 1 week. Participants were followed up for onset of depressive symptoms for 2 years at 6-monthly intervals. RESULTS: Daily-life walking (ie, gait intensity in the mediolateral axis, daily step counts, duration of longest walk) and self-rated health predicted the new onset of depressive symptoms at 2 years in univariable logistic regression models. In multivariable models containing a self-rated health measure, clinical mobility tests were not predictive of the onset of depressive symptoms. In contrast, a measure of daily-life walking (duration of longest walking bout) was identified as a significant predictor of depressive symptom onset [standardized odds ratio (SOR) 2.44, 95% CI 1.62-3.76] independent of self-rated health (SOR 1.51, 95% CI 1.16-1.96), with these 2 measures achieving a satisfactory prediction accuracy (area under the curve = 0.67, sensitivity: 0.78, specificity: 0.52). CONCLUSIONS AND IMPLICATIONS: A risk algorithm based on daily-life walking bouts and self-reported health demonstrated good accuracy for the prediction of depression onset in older people over 2 years. Wearable sensor data compared favorably with clinical mobility screens and may add important independent information for screening for depression among older people.

Prevalence/Incidence of Low Back Pain and Associated Risk Factors Among Nursing and Medical Students: A Systematic Review and Meta-Analysis.

OBJECTIVE: To summarize evidence regarding the prevalence and incidence of low back pain and associated risk factors in nursing and medical students. TYPE: Systematic review and meta-analysis. LITERATURE SURVEY: The protocol was registered with PROSPERO (CRD42015029729). Its reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Seven databases were searched until August 2020 to identify relevant studies. METHODOLOGY: Two independent reviewers screened, extracted, and evaluated the risk of bias of the selected studies. Meta-analyses were used to estimate 12-month prevalence/incidence rates of low back pain and associated risk factors in these students. Levels of evidence for risk factors were determined by the updated Guidelines for Systematic Reviews in the Cochrane Collaboration Back Review Group. SYNTHESIS: Sixteen studies involving 7072 students were included. The pooled 12-month prevalence rates of low back pain for nursing and medical students were 44% (95% confidence interval [95% CI]: 27%-61%) and 53% (95% CI: 44%-62%), respectively. The 12-month incidence of low back pain in nursing students ranged from 29% to 67%. No incidence rate was reported in medical students. Strong/moderate-quality evidence supported that final year of study (pooled odds ratio [OR] from five studies, 1.96, 95% CI: 1.13-3.40), anxiety (OR ranging from 3.12 to 4.61), or high mental pressure or psychological distress (OR ranging from 1.37 to 4.52) was associated with a higher 12-month low back pain prevalence in both student groups. Moderate-quality evidence suggested that prior history of low back pain (pooled OR from two studies: 3.46, 95% CI: 1.88-6.36) was associated with a higher 12-month low back pain incidence in nursing students. Similarly, moderate-quality evidence suggested that female medical students (pooled OR from two studies: 1.77, 95% CI: 1.09-2.86) demonstrated a higher 12-month low back pain prevalence than male counterparts. CONCLUSIONS: Although it is impossible to alter nonmodifiable risk factors for low back pain, universities may develop and implement proper strategies to mitigate modifiable risk factors in these students.

Associations between sport participation and knee symptoms: a cross-sectional study involving 3053 undergraduate students.

BACKGROUND: While a number of studies have investigated knee symptoms among elite athletes, few have directly compared the association between engagement in different sports and knee symptoms among young adults in the general population. The current study aimed to investigate the relation between sports participation hours, type/ number of sports engaged, self-rated competitiveness and knee symptoms among undergraduates. METHODS: Undergraduates were invited to participate in a self-administered online survey through invitation emails. Respondents were instructed to provide demographic information (e.g., age, gender, sports participation hours, types of engaged sports, self-rated competitiveness in sports and anxiety level etc.) and to report knee symptoms (current, the last 7 days, the last 12 months, and lifetime). Multiple logistic regressions were conducted to investigate the association between sports participation and current knee symptoms. RESULTS: Of 17,552 invitees, 3744 responded to the survey. Valid data from 3053 respondents was used for analysis. Forty-four percent of the respondents engaged in sports regularly (≥once per week). Running, cross-training and swimming were the most frequently participated sports among the respondents. The current prevalence rate of knee symptoms was 6.4%. Hours spent participating in combat sports, soccer, yoga, and basketball participation hours were significantly associated with current knee symptoms. Respondents who rated themselves as "competitive" demonstrated a higher risk of having current knee symptoms than "recreational" players. Number of engaged sports was not associated with current knee symptoms among undergraduates. CONCLUSIONS: Certain sports types were associated with current knee symptoms. Compared to self-rated "recreational" players, self-rated "competitive" players were more likely to have current knee symptoms. Students should take preventive measures to minimize their risk of developing knee symptoms, especially when participating in combat sports, soccer, yoga, and basketball, or engaging in sports at a highly competitive level.