Development of a clinical prediction model for the onset of functional decline in people aged 65-75 years: pooled analysis of four European cohort studies

Function Impairment Screening Tool predicts eight-year mortality in older adults: Beijing Longitudinal Study of Aging

PURPOSE

Function impairment is an early stage of disability in older adults and requires timely intervention. We have previously developed Function Impairment Screening Tool (FIST) based on the Delphi method, which has good reliability and validity, but the predictive effect is unknown. Therefore, we aimed to explore the role of FIST in predicting long-term mortality in community-dwelling older adults.

PARTICIPANTS AND METHODS

Data were from the Beijing Longitudinal Study of Aging. A total of 1,833 older adults with 8 years of follow-up were included. Function impairment was assessed using FIST. Cox proportional hazards model was used to calculate the predictive effect of FIST on 8-year all-cause mortality.

RESULTS

According to FIST, approximately half of the older adults had function impairment (47.6%). The prevalence of function impairment varied across populations. Logistic regression analysis showed that age, female, rural, poor health satisfaction, not drinking tea, and low Mini-Mental State Examination and intrinsic capacity score were associated with function impairment. Furthermore, function impairment was associated with poor physical function and high mortality. Cox analysis showed that FIST could predict 8-year mortality (hazard ratio [HR] = 3.26, 95% confidence interval [CI] 2.74-3.87), and this relationship persisted after adjusting for age, sex, area, marital status, live alone, educational level, smoking, drinking alcohol, and chronic diseases (HR = 1.79, 95% CI 1.45-2.17).

DISCUSSION

FIST can predict 8-year mortality in community-dwelling older adults. More attention should be paid to older adults with function impairment and early intervention should be provided.

Risk prediction models for disability in older adults: a systematic review and critical appraisal

BACKGROUND

The amount of prediction models for disability in older adults is increasing but the prediction performance of different models varies greatly, and the quality of prediction models is still unclear.

OBJECTIVES

To systematically review and critically appraise the studies on risk prediction models for disability in older adults.

METHODS

A systematic literature search was conducted on PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Wanfang Database, published up until June 30, 2023. Data were extracted according to the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the included studies. In addition, all included studies were evaluated for clinical value.

RESULTS

A total of 5722 articles were initially retrieved from databases, 16 studies and 17 prediction models were finally included after screening. The sample sizes of studies ranged from 420 to 90,889. Model development methods mainly included logistic regression analysis, Cox proportional hazards regression, and machine learning methods. The C statistic or area under the curve (AUC) of models ranged from 0.650 to 0.853, and nine models had C statistic/AUC higher than 0.75. Age, chronic disease, gender, self-rated health, body mass index (BMI), drinking, smoking and education level were the most common predictors. According to the PROBAST, all included studies were at high risk of bias, and 10 studies were at high concerns for applicability. Only two studies reported following the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. After evaluation, only two models reached the standard of clinical value.

CONCLUSION

Although most of the included prediction models had acceptable discrimination, the overall quality and clinical value of the current studies were poor. In the future, researchers should follow the TRIPOD statement and PROBAST checklist to develop prediction models with larger sample sizes, more reasonable study designs, and more scientific analysis methods, to improve the predictive performance and application value.

TRIAL REGISTRATION

The review protocol was registered in PROSPERO (registration ID: CRD42023446657).

Correlation of muscle strength, working memory, and activities of daily living in older adults

Objective

This study aims to investigate the relationship between muscle strength, working memory, and activities of daily living (ADL) in older adults. Additionally, it seeks to clarify the pathways and effects of working memory in mediating the relationship between muscle strength and ADL.

Methods

Using a cross-sectional study design, we recruited 245 older adults individuals from nursing homes. We collected data on grip strength, the 30-s sit-to-stand test, the N-back task, and ADL. The data were analyzed using independent sample t-tests, χ2 tests, correlation analysis, and structural equation modeling.

Results

Grip strength significantly influenced ADL (effect size = -0.175, 95% CI: -0.226 to -0.124). Grip strength also had a significant direct effect on ADL (effect size = -0.114, 95% CI: -0.161 to -0.067). The 1-back task correct rate significantly mediated the relationship between grip strength and ADL (effect size = 0.054, 95% CI: -0.084 to -0.029). The 30-s sit-to-stand test significantly impacted ADL (effect size = -0.280, 95% CI: -0.358 to -0.203). It also had a significant direct effect on ADL (effect size = -0.095, 95% CI: -0.183 to -0.007). The 1-back task correct rate significantly mediated the relationship between the 30-s sit-to-stand test and ADL (effect size = -0.166, 95% CI: -0.236 to -0.106).

Conclusion

There exists a strong correlation between muscle strength, working memory, and ADL. Increased muscle strength leads to better ADL performance and improved working memory tasks. Low cognitive load working memory tasks can mediate the relationship between muscle strength and ADL. Regular physical exercise can enhance muscle strength, slow down the decline of working memory, thereby maintaining or improving ADL in older adults.

Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study

This study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing guidance for long-term care plans. Based on the government-led long-term care insurance program in a pilot city of China from 2017 and followed up to 2021, the study included 42,353 disabled adults aged over 65, with 25,071 assigned to the training set and 17,282 to the validation set. The administrative data (including baseline characteristics, underlying medical conditions, and all-cause mortality) were collected to develop a deep learning model by least absolute shrinkage and selection operator. After a median follow-up time of 14 months, 17,565 (41.5%) deaths were recorded. Thirty predictors were identified and included in the final models for disability-related deaths. Physical disability (mobility, incontinence, feeding), adverse events (pressure ulcers and falls from bed), and cancer were related to poor prognosis. A total of 10,127, 25,140 and 7086 individuals were classified into low-, medium-, and high-risk groups, with actual risk probabilities of death of 9.5%, 45.8%, and 85.5%, respectively. This deep learning model could facilitate the prevention of risk factors and provide guidance for long-term care model planning based on risk stratification.

Effects of Cognitive-Motor and Motor-Motor Dual Tasks on Gait Performance in Older Adults with Sarcopenia

BACKGROUND

With the advent of global aging, the health of the older population has become a critical public health challenge. The purpose of this study was to investigate the effect of dual-tasking on gait performance in patients with sarcopenia.

METHODS

Thirty participants with sarcopenia (age: 70.73 ± 4.12 yr, MMSE score: 26.90 ± 3.00), including 14 males and 16 females, were selected according to the diagnostic criteria of the Asian Working Group on Sarcopenia. All participants were instructed to perform the gait test in three modes: single task (ST), cognitive-motor dual task (CMDT), and motor-motor dual task (MMDT). Statistical analyses were performed using one-way ANOVA to evaluate the effects of different task types on gait parameters of the participants.

RESULTS

(1) Compared with ST walking, gait frequency, step length, and step speed decreased, and the gait cycle and double-support phase increased in patients with sarcopenia during dual-task walking (p < 0.05); (2) Compared with ST walking, gait variability indices such as stride frequency, stride length, and support period significantly increased in patients with sarcopenia during dual-task walking (p < 0.05).

CONCLUSIONS

The increased difficulty in postural control caused by dual-task interference may reduce the safety of motor strategies in patients with sarcopenia and increase the risk of falls. Future studies should focus on the effects of exercise interventions on multitasking patterns in people with sarcopenia to promote balance function in these populations.

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed

OBJECTIVES

Risk of bias assessments are important in meta-analyses of both aggregate and individual participant data (IPD). There is limited evidence on whether and how risk of bias of included studies or datasets in IPD meta-analyses (IPDMAs) is assessed. We review how risk of bias is currently assessed, reported, and incorporated in IPDMAs of test accuracy and clinical prediction model studies and provide recommendations for improvement.

STUDY DESIGN AND SETTING

We searched PubMed (January 2018-May 2020) to identify IPDMAs of test accuracy and prediction models, then elicited whether each IPDMA assessed risk of bias of included studies and, if so, how assessments were reported and subsequently incorporated into the IPDMAs.

RESULTS

Forty-nine IPDMAs were included. Nineteen of 27 (70%) test accuracy IPDMAs assessed risk of bias, compared to 5 of 22 (23%) prediction model IPDMAs. Seventeen of 19 (89%) test accuracy IPDMAs used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), but no tool was used consistently among prediction model IPDMAs. Of IPDMAs assessing risk of bias, 7 (37%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided details on the information sources (e.g., the original manuscript, IPD, primary investigators) used to inform judgments, and 4 (21%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided information or whether assessments were done before or after obtaining the IPD of the included studies or datasets. Of all included IPDMAs, only seven test accuracy IPDMAs (26%) and one prediction model IPDMA (5%) incorporated risk of bias assessments into their meta-analyses. For future IPDMA projects, we provide guidance on how to adapt tools such as Prediction model Risk Of Bias ASsessment Tool (for prediction models) and QUADAS-2 (for test accuracy) to assess risk of bias of included primary studies and their IPD.

CONCLUSION

Risk of bias assessments and their reporting need to be improved in IPDMAs of test accuracy and, especially, prediction model studies. Using recommended tools, both before and after IPD are obtained, will address this.

Development and External Validation of Models to Predict Need for Nursing Home Level of Care in Community-Dwelling Older Adults With Dementia

Importance

Most older adults living with dementia ultimately need nursing home level of care (NHLOC).

Objective

To develop models to predict need for NHLOC among older adults with probable dementia using self-report and proxy reports to aid patients and family with planning and care management.

Design, Setting, and Participants

This prognostic study included data from 1998 to 2016 from the Health and Retirement Study (development cohort) and from 2011 to 2019 from the National Health and Aging Trends Study (validation cohort). Participants were community-dwelling adults 65 years and older with probable dementia. Data analysis was conducted between January 2022 and October 2023.

Exposures

Candidate predictors included demographics, behavioral/health factors, functional measures, and chronic conditions.

Main Outcomes and Measures

The primary outcome was need for NHLOC defined as (1) 3 or more activities of daily living (ADL) dependencies, (2) 2 or more ADL dependencies and presence of wandering/need for supervision, or (3) needing help with eating. A Weibull survival model incorporating interval censoring and competing risk of death was used. Imputation-stable variable selection was used to develop 2 models: one using proxy responses and another using self-responses. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (calibration plots).

Results

Of 3327 participants with probable dementia in the Health and Retirement Study, the mean (SD) age was 82.4 (7.4) years and 2301 (survey-weighted 70%) were female. At the end of follow-up, 2107 participants (63.3%) were classified as needing NHLOC. Predictors for both final models included age, baseline ADL and instrumental ADL dependencies, and driving status. The proxy model added body mass index and falls history. The self-respondent model added female sex, incontinence, and date recall. Optimism-corrected iAUC after bootstrap internal validation was 0.72 (95% CI, 0.70-0.75) in the proxy model and 0.64 (95% CI, 0.62-0.66) in the self-respondent model. On external validation in the National Health and Aging Trends Study (n = 1712), iAUC in the proxy and self-respondent models was 0.66 (95% CI, 0.61-0.70) and 0.64 (95% CI, 0.62-0.67), respectively. There was excellent calibration across the range of predicted risk.

Conclusions and Relevance

This prognostic study showed that relatively simple models using self-report or proxy responses can predict need for NHLOC in community-dwelling older adults with probable dementia with moderate discrimination and excellent calibration. These estimates may help guide discussions with patients and families in future care planning.

Muscular strength, mobility in daily life and mental wellbeing among older adult Inuit in Greenland. The Greenland population health survey 2018

The purpose was to analyse the association of muscular strength, muscle pain and reduced mobility in daily life with mental wellbeing among older Inuit men and women in Greenland. Data (N = 846) was collected as part of a countrywide cross-sectional health survey in 2018. Hand grip strength and 30-seconds chair stand test were measured according to established protocols. Mobility in daily life was assessed by five questions about the ability to perform specific activities of daily living. Mental wellbeing was assessed by questions about self-rated health, life satisfaction and Goldberg's General Health Questionnaire. In binary multivariate logistic regression models adjusted for age and social position, muscular strength (OR 0.87-0.94) and muscle pain (OR 1.53-1.79) were associated with reduced mobility. In fully adjusted models, muscle pain (OR 0.68-0.83) and reduced mobility (OR 0.51-0.55) but were associated with mental wellbeing. Chair stand score was associated with life satisfaction (OR 1.05). With an increasingly sedentary lifestyle, increasing prevalence of obesity and increasing life expectancy the health consequences of musculoskeletal dysfunction are expected to grow. Prevention and clinical handling of poor mental health among older adults need to consider reduced muscle strength, muscle pain and reduced mobility as important determinants.

Predicting functional dependency using machine learning among a middle-aged and older Chinese population

OBJECTIVE

To develop prediction models for assessing functional dependency in a middle-aged and older Chinese population.

METHOD

Adults ≥45 years old from the China Health and Retirement Longitudinal Study (CHARLS) and without functional dependency at baseline were included. Functional dependency was defined as needing any help in any basic activities of daily living (ADL) or instrumental activities of daily living (IADL). The outcomes were overall functional dependency, ADL and IADL dependency. Stacked ensemble models were constructed based on five selected machine learning models. Models were trained and tested in the 2011-2015 cohort, and were externally validated in the 2015-2018 cohort. SHapley Additive exPlanations (SHAP) was utilized to quantify the significance of predictors.

RESULT

In the training cohort, a total of 6,297 participants were included at baseline, 1,893 developed functional dependency during the follow-up period. The stacked ensemble model achieved the best performance in terms of discrimination ability for predicting overall functional dependency, ADL and IADL dependency, with AUCs of 0.750, 0.690 and 0.748, respectively; in external validation cohort, the corresponding AUCs were 0.725, 0.719 and 0.727, respectively. A compact model was further developed and maintained similar predictive performance.

CONCLUSION

The stacked ensemble approach can serve as a useful tool for identifying the risk of functional dependency in a large Chinese population. For ADL dependency, arthritis, age, self-report health, and waist circumference were identified as highly significant predictors. Conversely, cognitive function, age, living in rural areas, and performance in chair stand test emerged as highly ranked predictors for IADL dependency.

The effect of long-term care insurance on healthcare utilization of middle-aged and older adults: evidence from China health and retirement longitudinal study

BACKGROUND

As global ageing continues to increase and many countries face challenges from the growing demand for long-term care. Drawing on the experiences of developed countries, developing countries have explored their own suitable long-term care insurance and have shown strong potential for development and research prospects. However, due to their late start, relevant research is underrepresented in the global research network and still needs to be supplemented. The present study hopes to examine the effect of long-term care insurance on healthcare utilization among the middle-aged and elderly from an empirical perspective, using China as an example.

METHODS

Panel data from wave 3 (2015) and wave 4 (2018) of the nationally-representative China health and retirement longitudinal study were selected to obtain a sample of 661 processing participants and 16,065 control participants after matching the policy implementation time in the first pilot cities, and quantitative analysis was conducted using difference-in-differences propensity score matching estimator method to assess the net effect of long-term care insurance on health care utilization among the middle-aged and elderly adults.

RESULTS

In the matched frequency-weighted regression difference-in-differences estimator results, long-term care insurance had a negative effect on the number and costs of annual hospitalizations at the 5% significance level (key variable values of - 0.0568101 and - 1236.309, respectively) and a non-significant effect on outpatient service utilization (P > 0.05). Further exploration of the heterogeneous effect of it revealed that implementation had a more significant negative effect on hospitalization utilization for middle-aged and older people in the East and for those with higher levels of education or attended care.

CONCLUSION

Long-term care insurance has played a role in controlling hospitalization costs but has not yet achieved the expected effect in controlling outpatient costs. The policy effects in terms of regional distribution and education level and care situation have been variable. The treatment plan of long-term care insurance needs to be improved, the supply of resources for long-term care services should be increased, and the promotion of long-term care insurance and health science should be given attention.

Clinical frailty scale is useful in predicting return-to-home in patients admitted due to coronavirus disease

BACKGROUND

The spread of the novel severe acute respiratory syndrome coronavirus 2 infection has been prolonged, with the highly contagious Omicron variant becoming the predominant variant by 2022. Many patients admitted to dedicated coronavirus disease 2019 (COVID-19) wards (COVID-19 treatment units) develop disuse syndrome while being treated in the hospital, and their ability to perform activities of daily living declines, making it difficult for hospitals to discharge them. This study aimed to investigate the relationship between the degree of frailty and home discharge of patients admitted to a COVID-19 treatment units.

METHODS

This study retrospectively examined the in-patient medical records of 138 patients (82.7 ± 7.6 years old) admitted to a COVID-19 treatment unit from January to December 2022. The end-point was to determine the patients' ability to be discharged from the unit directly to home; such patients were classified into the 'Home discharge' group and compared with those in the 'Difficulty in discharge' group. The degree of frailty was determined based on the Clinical Frailty Scale (CFS), and the relationship with the endpoint was analysed. A receiver operating characteristic (ROC) curve was created and the cut-off value was calculated with the possibility of home discharge as the state variable and CFS as the test variable. Logistic regression analysis was conducted with the possibility of home discharge as the dependent variable and CFS as the independent variable.

RESULTS

There were 75 patients in the Home discharge group and 63 in the Difficulty in discharge group. ROC analysis showed a CFS cut-off value of 6 or more, with a sensitivity of 70.7% and a specificity of 84.1%. The results of the logistic regression analysis showed a significant correlation between possibility of home discharge and CFS even after adjusting for covariates, with an odds ratio of 13.44.

CONCLUSIONS

Based on the evaluation of the degree of frailty conducted in the COVID-19 treatment unit, it was possible to accurately predict whether a patient could be discharged directly to home after treatment CFS could be an effective screening tool to easily detect patients requiring ongoing hospitalisation even after the acute phase of treatment.

Development and validation of an intrinsic capacity composite score in the Longitudinal Aging Study Amsterdam: a formative approach

BACKGROUND

Intrinsic capacity (IC) defined by the WHO refers to the composite of five domains of capacities. So far, developing and validating a standardized overall score of the concept have been challenging partly because its conceptual framework has been unclear. We consider that a person's IC is determined by its domain-specific indicators suggesting a formative measurement model.

AIMS

To develop an IC score applying a formative approach and assess its validity.

METHODS

The study sample (n = 1908) consisted of 57-88-year-old participants from the Longitudinal Aging Study Amsterdam (LASA). We used logistic regression models to select the indicators to the IC score with 6-year functional decline as an outcome. An IC score (range 0-100) was constructed for each participant. We examined the known-groups' validity of the IC score by comparing groups based on age and number of chronic diseases. The criterion validity of the IC score was assessed with 6-year functional decline and 10-year mortality as outcomes.

RESULTS

The constructed IC score included seven indicators covering all five domains of the construct. The mean IC score was 66.7 (SD 10.3). The scores were higher among younger participants and those who had lower number of chronic diseases. After adjustment for sociodemographic indicators, chronic diseases, and BMI, a one-point higher IC score was associated with a 7% decreased risk for 6-year functional decline and a 2% decreased risk for 10-year mortality.

CONCLUSIONS

The developed IC score demonstrated discriminative ability according to age and health status and is associated with subsequent functional decline and mortality.

Reinvest to Assess: Advancing Approaches to Motor Competence Measurement Across the Lifespan

Measurement of motor competence is a vital process to advancing knowledge in the field of motor development. As motor competence is being more widely linked to research in other academic domains (e.g., public health, neuroscience, behavioral health), it is imperative that measurement methodology and protocols are reproducible with high degrees of validity and reliability. When addressing the plethora of available assessments, mostly developed for youth populations, there are potential questions and concerns that need to be addressed and/or clarified. One of the most prominent issues is the lack of a lifespan measure of motor competence, which is at odds with the premise of the field of motor development-studying changes in motor behavior across the lifespan. We address six areas of concern in lifespan assessment which include: (1) lack of assessment feasibility for conducting research with large samples, (2) lack of accountability for cultural significance of skills assessed, (3) limited sensitivity and discriminatory capabilities of assessments, (4) developmental and ecological validity limitations, (5) a problematic definition of 'success' in skill performance, and (6) task complexity and adaptability limitations. It is important to critically analyze current assessment methodologies as it will help us to envision the development and application of potential new assessments through a more comprehensive lens. Ultimately, we propose that reinvesting in how we think about assessment will be highly beneficial for integrating motor development from a holistic perspective, impact scientific advancements in other developmental domains, and increase global and lifespan surveillance of motor competence.

Association of clinical outcome assessments of mobility capacity and incident disability in community-dwelling older adults - a systematic review and meta-analysis

The objective of the present review is to synthesize all available research on the association between mobility capacity and incident disability in non-disabled older adults. MEDLINE, EMBASE and CINAHL databases were searched without any limits or restrictions until February 2021. Published reports of longitudinal cohort studies that estimated a direct association between baseline mobility capacity, assessed with a standardized outcome assessment, and subsequent development of disability, including initially non-disabled older adults were included. The risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool. Random-effect models were used to explore the objective. The certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. The main outcome measures were the pooled relative risks (RR) per one conventional unit per mobility assessment for incident disability. A total of 40 reports (85,515 participants at baseline) were included. For usual and fast gait speed, the RR per -0.1 m/s was 1.23 (95% CI: 1.18-1.28; 26,638 participants) and 1.28 (95% CI: 1.19-1.38; 8161 participants), respectively. Each point decrease in Short Physical Performance Battery score increased the risk of incident disability by 30% (RR = 1.30, 95% CI: 1.23-1.38; 9183 participants). The RR of incident disability by each second increase in Timed Up and Go test and Chair Rise Test performance was 1.15 (95% CI: 1.09-1.21; 30,426 participants) and 1.07 (95% CI: 1.04-1.10; 9450 participants), respectively. The review concludes that among community-dwelling non-disabled older adults, poor mobility capacity is a potent modifiable risk factor for incident disability. Mobility impairment should be mandated as a quality indicator of health for older people.

Impact of adherence to a lifestyle-integrated programme on physical function and behavioural complexity in young older adults at risk of functional decline: a multicentre RCT secondary analysis

CONTEXT

Long-term adherence to physical activity (PA) interventions is challenging. The Lifestyle-integrated Functional Exercise programmes were adapted Lifestyle-integrated Functional Exercise (aLiFE) to include more challenging activities and a behavioural change framework, and then enhanced Lifestyle-integrated Functional Exercise (eLiFE) to be delivered using smartphones and smartwatches.

OBJECTIVES

To (1) compare adherence measures, (2) identify determinants of adherence and (3) assess the impact on outcome measures of a lifestyle-integrated programme.

DESIGN, SETTING AND PARTICIPANTS

A multicentre, feasibility randomised controlled trial including participants aged 61-70 years conducted in three European cities.

INTERVENTIONS

Six-month trainer-supported aLiFE or eLiFE compared with a control group, which received written PA advice.

OUTCOME MEASURES

Self-reporting adherence per month using a single question and after 6-month intervention using the Exercise Adherence Rating Scale (EARS, score range 6-24). Treatment outcomes included function and disability scores (measured using the Late-Life Function and Disability Index) and sensor-derived physical behaviour complexity measure. Determinants of adherence (EARS score) were identified using linear multivariate analysis. Linear regression estimated the association of adherence on treatment outcome.

RESULTS

We included 120 participants randomised to the intervention groups (aLiFE/eLiFE) (66.3±2.3 years, 53% women). The 106 participants reassessed after 6 months had a mean EARS score of 16.0±5.1. Better adherence was associated with lower number of medications taken, lower depression and lower risk of functional decline. We estimated adherence to significantly increase basic lower extremity function by 1.3 points (p<0.0001), advanced lower extremity function by 1.0 point (p<0.0001) and behavioural complexity by 0.008 per 1.0 point higher EARS score (F(3,91)=3.55, p=0.017) regardless of group allocation.

CONCLUSION

PA adherence was associated with better lower extremity function and physical behavioural complexity. Barriers to adherence should be addressed preintervention to enhance intervention efficacy. Further research is needed to unravel the impact of behaviour change techniques embedded into technology-delivered activity interventions on adherence.

TRIAL REGISTRATION NUMBER

NCT03065088.

Detecting disabilities in everyday life: evidence from a geriatric assessment

BACKGROUND

The activities of daily living (ADL) score is a widely used index to establish the degree of independence from any help in everyday life situations. Measuring ADL accurately is time-consuming and costly. This paper presents a framework to approximate ADL via variables usually collected in comprehensive geriatric assessments. We show that the selected variables serve as good indicators in explaining the physical disabilities of older patients.

METHODS

Our sample included information from a geriatric assessment of 326 patients aged between 64 and 99 years in a hospital in Tyrol, Austria. In addition to ADL, 23 variables reflecting the physical and mental status of these patients were recorded during the assessment. We performed least absolute shrinkage and selection operator (LASSO) to determine which of these variables had the highest impact on explaining ADL. Then, we used receiver operating characteristic (ROC) analysis and logistic regression techniques to validate our model performance. Finally, we calculated cut-off points for each of the selected variables to show the values at which ADL fall below a certain threshold.

RESULTS

Mobility, urinary incontinence, nutritional status and cognitive function were most closely related to ADL and, therefore, to geriatric patients' functional limitations. Jointly, the selected variables were able to detect neediness with high accuracy (area under the ROC curve (AUC) = 0.89 and 0.91, respectively). If a patient had a limitation in one of these variables, the probability of everyday life disability increased with a statistically significant factor between 2.4 (nutritional status, 95%-CI 1.5-3.9) and 15.1 (urinary incontinence, 95%-CI 3.6-63.4).

CONCLUSIONS

Our study highlights the most important impairments of everyday life to facilitate more efficient use of clinical resources, which in turn allows for more targeted treatment of geriatric patients. At the patient level, our approach enables early detection of functional limitations and timely indications of a possible need for assistance in everyday life.

Predictors for the Transitions of Poor Clinical Outcomes Among Geriatric Rehabilitation Inpatients

OBJECTIVE

To investigate the associations of morbidity burden and frailty with the transitions between functional decline, institutionalization, and mortality.

DESIGN

REStORing health of acutely unwell adulTs (RESORT) is an ongoing observational, longitudinal inception cohort and commenced on October 15, 2017. Consented patients were followed for 3 months postdischarge.

SETTING AND PARTICIPANTS

Consecutive geriatric rehabilitation inpatients admitted to geriatric rehabilitation wards.

METHODS

Patients' morbidity burden was assessed at admission using the Charlson Comorbidity Index (CCI) and Cumulative Illness Rating Scale (CIRS). Frailty was assessed using the Clinical Frailty Scale (CFS) and modified Frailty Index based on laboratory tests (mFI-lab). A multistate model was applied at 4 time points: 2 weeks preadmission, admission, and discharge from geriatric rehabilitation and 3 months postdischarge, with the following outcomes: functional decline, institutionalization, and mortality. Cox proportional hazards regression was applied to investigate the associations of morbidity burden and frailty with the transitions between outcomes.

RESULTS

The 1890 included inpatients had a median age of 83.4 (77.6-88.4) years, and 56.3% were female. A higher CCI score was associated with a greater risk of transitions from preadmission and declined functional performance to mortality [hazard ratio (HR) 1.28, 95% CI 1.03-1.59; HR 1.32, 95% CI 1.04-1.67]. A higher CIRS score was associated with a higher risk of not recovering from functional decline (HR 0.80, 95% CI 0.69-0.93). A higher CFS score was associated with a greater risk of transitions from preadmission and declined functional performance to institutionalization (HR 1.28, 95% CI 1.10-1.49; HR 1.23, 95% CI 1.04-1.44) and mortality (HR 1.12, 95% CI 1.01-1.33; HR 1.11, 95% CI 1.003-1.31). The mFI-lab was not associated with any of the transitions. None of the morbidity measures or frailty assessment tools were associated with the transitions from institutionalization to other outcomes.

CONCLUSIONS AND IMPLICATIONS

This study demonstrates that greater frailty severity, assessed using the CFS, is a significant risk factor for poor clinical outcomes and demonstrates the importance of implementing it in the geriatric rehabilitation setting.

Prediction models for functional status in community dwelling older adults: a systematic review

Background

Disability poses a burden for older persons, and is associated with poor outcomes and high societal costs. Prediction models could potentially identify persons who are at risk for disability. An up to date review of such models is missing.

Objective

To identify models developed for the prediction of functional status in community dwelling older persons.

Methods

A systematic review was performed including studies of older persons that developed and/or validated prediction models for the outcome functional status. Medline and EMBASE were searched, and reference lists and prospective citations were screened for additional references. Risk of bias was assessed using the PROBAST-tool. The performance of models was described and summarized, and the use of predictors was collated using the bag-of-words text mining procedure.

Results

Forty-three studies were included and reported 167 evaluations of prediction models. The median c-statistic values for the multivariable development models ranged between 0.65 and 0.76 (minimum = 0.58, maximum = 0.90), and were consistently higher than the values of the validation models for which median c-statistic values ranged between 0.6 and 0.68 (minimum = 0.50, maximum = 0.81). A total of 559 predictors were used in the models. The five predictors most frequently used were gait speed (n = 47), age (n = 38), cognition (n = 27), frailty (n = 24), and gender (n = 22).

Conclusions

No model can be recommended for implementation in practice. However, frailty models appear to be the most promising, because frailty components (e.g. gait speed) and frailty indexes demonstrated good to excellent predictive performance. However, the risk of study bias was high. Substantial improvements can be made in the methodology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03156-7.

Predicting Future Mobility Limitation in Older Adults: A Machine Learning Analysis of Health ABC Study Data

BACKGROUND

Mobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting future mobility limitation in older adults using repeated measures data.

METHODS

We used annual assessments over 9 years of follow-up from the Health, Aging, and Body Composition study to model mobility limitation, defined as self-report of any difficulty walking a quarter mile or climbing 10 steps. We considered 46 predictors, including demographics, lifestyle, chronic conditions, and physical function. With a split sample approach, we developed mixed models (generalized linear and Binary Mixed Model forest) using (a) all 46 predictors, (b) a variable selection algorithm, and (c) the top 5 most important predictors. Age was included in all models. Performance was evaluated using area under the receiver operating curve in 2 internal validation data sets.

RESULTS

Area under the receiver operating curve ranged from 0.80 to 0.84 for the models. The most important predictors of mobility limitation were ease of getting up from a chair, gait speed, self-reported health status, body mass index, and depression.

CONCLUSIONS

Machine learning models using repeated measures had good performance for identifying older adults at risk of developing mobility limitation. Future studies should evaluate the utility and efficiency of the prediction models as a tool in clinical settings for identifying at-risk older adults who may benefit from interventions aimed to prevent or delay mobility limitation.

Novel physical performance-based models for activities of daily living disability prediction among Chinese older community population: a nationally representative survey in China

Background

Physical performances including upper and lower limb functions have predictive roles in activities of daily living (ADL) disability, but they have rarely been incorporated into prediction models. This study primarily aimed to develop and validate novel physical performance-based models for ADL disability among Chinese older adults. Comparisons of predictive performance across multiple models were performed, and model simplification was further explored.

Methods

Data were obtained from the China Health and Retirement Longitudinal Study in the 2011 and 2015 waves, containing 2192 older adults over 60 years old. Our models were constructed by logistic regression analysis, using a backward stepwise selection. Model performance was internally validated by discrimination, calibration, and clinical utility. Integrated Discrimination Improvement (IDI) and Net Reclassification Improvement (NRI) were used to assess the incremental benefit of the extended models. Moreover, nomograms were built for visualization.

Results

We selected gender, age, smoking, self-report health condition, BMI, depressive symptoms, and cognitive function into the fundamental model (Model 1). Based on Model 1, five novel prediction models were constructed by adding handgrip strength (Model 2), Short Physical Performance Battery (SPPB) (Model 3), gait speed (Model 4), handgrip strength plus SPPB (Model 5), and handgrip strength plus gait speed (Model 6), respectively. Significant improvement in predictive values were observed for all five novel models compared with Model 1 (C-index = 0.693). The lower limb model (Model 3 SPPB model: C-index = 0.731) may play a key role in the prediction of ADL disability, reflecting a comparable predictive value to the comprehensive models combining both upper and lower limbs (Model 5 handgrip strength + SPPB model: C-index = 0.732). When we simplified the lower limb models by replacing SPPB with gait speed, the predictive values attenuated slightly (C-index: Model 3 vs Model 4: 0.731 vs 0.714; Model 5 vs Model 6: 0.732 vs 0.718), but still better than the upper limb model (Model 2 handgrip strength model: C-index = 0.701).

Conclusions

Physical performance-based models, especially lower limb model, provided improved prediction for ADL disability among Chinese older adults, which may help guide the targeted intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02905-y.

Functional Differences Found in the Elderly Living in the Community

Introduction: Successful aging lies in cognitive and functional maintenance, and in the optimal performance of daily tasks that keep the elderly free of disability and dependence. However, there is little evidence for functional differences for gender and age, and how cognitive and physical demands in past working lives can affect them, to design more personalized occupational therapy interventions to prevent functional and cognitive impairment. Method: This observational descriptive study evaluated 367 older adults living in a community with subjective memory complaints and scored between 24 and 35 with the Spanish version of the “Mini-Mental State Examination (MEC-35)”. Basic activities of daily living (BADL) were studied with the Barthel Index, while instrumental ADL (IADL) were examined with the Lawton–Brody scale. Functional differences for gender, age, and physico-mental occupation were examined. Results: The significant differences found for gender indicated that men did better in BADL (p = 0.026) and women better performed IADL (p < 0.001). Differences between age groups suggest that the younger group (aged 64–75) obtained better results for BADL (p = 0.001) and IADL (p < 0.001). For physico-mental occupation, statistically significant differences were found only in IADL for mental (p = 0.034) and physical occupation (p = 0.005). Conclusions: Gender, age, and the cognitive and physical demands of occupational stages, can be important predictors of cognitive and functional impairment. These results can be generalized to other health centers in the province and to other Spanish Autonomous Communities because their socio-demographic variables are similar. It would be interesting to carry out multimodal personalized interventions that consider the factors that might affect functional impairment to preserve personal autonomy.

A web-based dynamic Nomogram for predicting instrumental activities of daily living disability in older adults: a nationally representative survey in China

Background

Instrumental Activities of Daily Living (IADL) disability is a common health burden in aging populations. The identification of high-risk individuals is essential for timely targeted interventions. Although predictors for IADL disability have been well described, studies constructing prediction tools for IADL disability among older adults were not adequately explored. Our study aims to develop and validate a web-based dynamic nomogram for individualized IADL disability prediction in older adults.

Methods

Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). We included 4791 respondents aged 60 years and over, without IADL disability at baseline in the 2011 to 2013 cohort (training cohort) and 371 respondents in the 2013 to 2015 cohort (validation cohort). Here, we defined IADL disability as needing any help in any items of the Lawton and Brody’s scale. A web-based dynamic nomogram was built based on a logistic regression model in the training cohort. We validated the nomogram internally with 1000 bootstrap resamples and externally in the validation cohort. The discrimination and calibration ability of the nomogram was assessed using the concordance index (C-index) and calibration plots, respectively.

Results

The nomogram incorporated ten predictors, including age, education level, social activity frequency, drinking frequency, smoking frequency, comorbidity condition, self-report health condition, gait speed, cognitive function, and depressive symptoms. The C-index values in the training and validation cohort were 0.715 (bootstrap-corrected C-index = 0.702) and 0.737, respectively. The internal and external calibration plots for predictions of IADL disability were in excellent agreement. An online web server was built (https://lilizhang.shinyapps.io/DynNomapp/) to facilitate the use of the nomogram.

Conclusions

We developed a dynamic nomogram to evaluate the risk of IADL disability precisely and expediently. The application of this nomogram would be helpful for health care physicians in decision-making.

Do morbidity measures predict the decline of activities of daily living and instrumental activities of daily living amongst older inpatients? A systematic review

OBJECTIVES

Older adults often suffer from multimorbidity, which results in hospitalisations. These are often associated with poor health outcomes such as functional dependence and mortality. The aim of this review was to summarise the current literature on the capacities of morbidity measures in predicting activities of daily living (ADL) and instrumental activities of daily living (IADL) amongst inpatients.

METHODS

A systematic literature search was performed using four databases: Medline, Cochrane, Embase, and Cinahl Central from inception to 6th March 2019. Keywords included comorbidity, multimorbidity, ADL, and iADL, along with specific morbidity measures. Articles reporting on morbidity measures predicting ADL and IADL decline amongst inpatients aged 65 years or above were included.

RESULTS

Out of 7334 unique articles, 12 articles were included reporting on 7826 inpatients (mean age 77.6 years, 52.7% females). Out of five morbidity measures, the Charlson Comorbidity Index was most often reported. Overall, morbidity measures were poorly associated with ADL and IADL decline amongst older inpatients.

CONCLUSION

Morbidity measures are poor predictors for ADL or IADL decline amongst older inpatients and follow-up duration does not alter the performance of morbidity measures.

Physical Activity as Moderator of the Association Between APOE and Cognitive Decline in Older Adults: Results from Three Longitudinal Cohort Studies

Background

Previous studies have suggested that the association between APOE ɛ ₄ and dementia is moderated by physical activity (PA), but the results remain inconclusive and longitudinal data on cognitive decline are missing. In this study, we examine whether there is a gene–environment interaction between APOE and PA on cognitive decline in older adults using 9-year follow-up data of three cohort studies.

Methods

We followed 7,176 participants from three longitudinal cohort studies: Longitudinal Aging Study Amsterdam (LASA), InCHIANTI, and Rotterdam Study for 9 years. PA was assessed with self-reported questionnaires and was categorized in low, moderate, and high PA. Cognitive function was assessed with the Mini-Mental State Examination (MMSE) and cognitive decline was defined as a decrease of three points or more on the MMSE during 3 years follow-up. We fitted logistic regression models using generalized estimating equations adjusting for age, sex, education, depressive symptoms, and number of chronic disease. Interaction between APOE and PA was tested on multiplicative and additive scale.

Results

Cohorts were similar in most aspects but InCHIANTI participants were on average older and had lower education. APOE ɛ ₄ carriers had higher odds of cognitive decline (odds ratio [OR] = 1.46, 95% confidence interval [CI]: 1.29–1.64) while PA was not significantly associated with cognitive decline overall (moderate PA: OR = 0.87, 0.67–1.13; high PA: OR = 0.71, 0.36–1.40). There was no evidence for an interaction effect between PA and APOE ɛ ₄ in cognitive decline in older adults (APOE × moderate PA: p = .83; APOE × high PA: p = .90).

Conclusions

Previous claims of a gene–environment interaction between APOE ɛ ₄ and PA in cognitive decline are not supported by our results.

Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients

Background

Assessment of functional ability, including activities of daily living (ADLs), is a manual process completed by skilled health professionals. In the presented research, an automated decision support tool, the Computational Barthel Index Tool (CBIT), was constructed that can automatically assess and predict probabilities of current and future ADLs based on patients’ medical history.

Methods

The data used to construct the tool include the demographic information, inpatient and outpatient diagnosis codes, and reported disabilities of 181,213 residents of the Department of Veterans Affairs’ (VA) Community Living Centers. Supervised machine learning methods were applied to construct the CBIT. Temporal information about times from the first and the most recent occurrence of diagnoses was encoded. Ten-fold cross-validation was used to tune hyperparameters, and independent test sets were used to evaluate models using AUC, accuracy, recall and precision. Random forest achieved the best model quality. Models were calibrated using isotonic regression.

Results

The unabridged version of CBIT uses 578 patient characteristics and achieved average AUC of 0.94 (0.93–0.95), accuracy of 0.90 (0.89–0.91), precision of 0.91 (0.89–0.92), and recall of 0.90 (0.84–0.95) when re-evaluating patients. CBIT is also capable of predicting ADLs up to one year ahead, with accuracy decreasing over time, giving average AUC of 0.77 (0.73–0.79), accuracy of 0.73 (0.69–0.80), precision of 0.74 (0.66–0.81), and recall of 0.69 (0.34–0.96). A simplified version of CBIT with 50 top patient characteristics reached performance that does not significantly differ from full CBIT.

Conclusion

Discharge planners, disability application reviewers and clinicians evaluating comparative effectiveness of treatments can use CBIT to assess and predict information on functional status of patients.

Computational assessment of MCM2 transcriptional expression and identification of the prognostic biomarker for human breast cancer

Minichromosome maintenance protein 2 (MCM2) is a highly conserved protein from the MCM protein family that plays an important role in eukaryotic DNA replication as well as in cell cycle progression. In addition, it maintains the ploidy level consistency in eukaryotic cells, hence, mutations or alteration of this protein could result in the disintegration of the fine-tuned molecular machinery that can lead to uncontrolled cell proliferation. Moreover, MCM2 has been found to be an important marker for progression and prognosis in different cancers. Therefore, we aimed to analyze the MCM2 expression and the associated outcome in breast cancer (BC) patients based on the publicly available online databases. In this study, server-based gene expression analyses indicate the upregulation of MCM2 (p < 10^-6; fold change>2.0) in various BC subtypes as compared to the respective normal tissues. Besides, the evaluation of histological sections from healthy and cancer tissues showed strong staining signals indicating higher expression of MCM2 protein. The overexpression of MCM2 was significantly correlated to promoter methylation and was related to patients' clinical features. Further, mutation analysis suggested missense as the predominant type of mutation (71.4%) with 18 copy-number alterations and 0.2% mutation frequency in the MCM2 gene. This study revealed a significant correlation (Cox p ≤ 0.05) between the higher MCM2 expression and lower patient survival. Finally, we identified the co-expressed genes with gene ontological features and signaling pathways associated in BC development. We believe that this study will provide a basis for MCM2 to be a significant biomarker for human BC prognosis.