Measuring Stress in Health Professionals Over the Phone Using Automatic Speech Analysis During the COVID-19 Pandemic: Observational Pilot Study

Fear of falling in community-dwelling older adults: What their gait acceleration pattern reveals

BACKGROUND

Fear of Falling (FOF) is common among community-dwelling older adults and is associated with increased fall-risk, reduced activity, and gait modifications.

OBJECTIVE

In this cross-sectional study, we examined the relationships between FOF and gait quality.

METHODS

Older adults (N=232; age 77±6; 65 % females) reported FOF by a single yes/no question. Gait quality was quantified as (1) harmonic ratio (smoothness) and other time-frequency spatiotemporal variables from triaxial accelerometry (Vertical-V, Mediolateral-ML, Anterior-Posterior -AP) during six-minute walk; (2) gait speed, step-time CoV (variability), and walk-ratio (step-length/cadence) on a 4-m instrumented walkway. Mann Whitney U-tests and Random forest classifier compared gait between those with and without FOF. Selected gait variables were used to build Support Vector Machine (SVM) classifier and performance was evaluated using AUC-ROC.

RESULTS

Individuals with FOF had slower gait speed (103.66 ± 17.09 vs. 110.07 ± 14.83 cm/s), greater step time CoV (4.17 ± 1.66 vs. 3.72 ± 1.24 %), smaller walk-ratio (0.53 ± 0.08 vs. 0.56 ± 0.07 cm/steps/minute), smaller standard deviation V (0.15 ± 0.06 vs. 0.18 ± 0.09 m/s2), and smaller harmonic-ratio V (2.14 ± 0.73 vs. 2.38 ± 0.58), all p<.01. Linear SVM yielded an AUC-ROC of 67 % on test dataset, coefficient values being gait speed (-0.19), standard deviation V (-0.23), walk-ratio (-0.36), and smoothness V (-0.38) describing associations with presence of FOF.

CONCLUSION

Older adults with FOF have reduced gait speed, acceleration adaptability, walk-ratio, and smoothness. Disrupted gait patterns during fear of falling could provide insights into psychosocial distress in older adults. Longitudinal studies are warranted.

Human behavior in the time of COVID-19: Learning from big data

Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups-using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

A rapid, non-invasive method for fatigue detection based on voice information

Fatigue results from a series of physiological and psychological changes due to continuous energy consumption. It can affect the physiological states of operators, thereby reducing their labor capacity. Fatigue can also reduce efficiency and, in serious cases, cause severe accidents. In addition, it can trigger pathological-related changes. By establishing appropriate methods to closely monitor the fatigue status of personnel and relieve the fatigue on time, operation-related injuries can be reduced. Existing fatigue detection methods mostly include subjective methods, such as fatigue scales, or those involving the use of professional instruments, which are more demanding for operators and cannot detect fatigue levels in real time. Speech contains information that can be used as acoustic biomarkers to monitor physiological and psychological statuses. In this study, we constructed a fatigue model based on the method of sleep deprivation by collecting various physiological indexes, such as P300 and glucocorticoid level in saliva, as well as fatigue questionnaires filled by 15 participants under different fatigue procedures and graded the fatigue levels accordingly. We then extracted the speech features at different instances and constructed a model to match the speech features and the degree of fatigue using a machine learning algorithm. Thus, we established a method to rapidly judge the degree of fatigue based on speech. The accuracy of the judgment based on unitary voice could reach 94%, whereas that based on long speech could reach 81%. Our fatigue detection method based on acoustic information can easily and rapidly determine the fatigue levels of the participants. This method can operate in real time and is non-invasive and efficient. Moreover, it can be combined with the advantages of information technology and big data to expand its applicability.

Validation of At-Home Application of a Digital Cognitive Screener for Older Adults

Standardized neuropsychological assessments of older adults are important for both clinical diagnosis and biobehavioral research. Over decades, in-person testing has been the basis for population normative values that rank cognitive performance by demographic status. Most recently, digital tools have enabled remote data collection for cognitive measures, which offers the significant promise to extend the basis for normative values to be more inclusive of a larger cross section of the older population. We developed a Remote Characterization Module (RCM), using a speech-to-text interface, as a novel digital tool to administer an at-home, 25-min cognitive screener that mimics eight standardized neuropsychological measures. Forty cognitively healthy participants were recruited from a longitudinal aging research cohort, and they performed the same measures of memory, attention, verbal fluency and set-shifting in both in-clinic paper-and-pencil (PAP) and at-home RCM versions. The results showed small differences, if any, for how participants performed on in-person and remote versions in five of eight tasks. Critically, robust correlations between their PAP and RCM scores across participants support the finding that remote, digital testing can provide a reliable assessment tool for rapid and remote screening of healthy older adults’ cognitive performance in several key domains. The implications for digital cognitive screeners are discussed.

In-Person and Remote Workshops for People With Neurocognitive Disorders: Recommendations From a Delphi Panel

Workshops using arts and board games are forms of non-pharmacological intervention widely employed in seniors with neurocognitive disorders. However, clear guidelines on how to conduct these workshops are missing. The objective of the Art and Game project (AGAP) was to draft recommendations on the structure and content of workshops for elderly people with neurocognitive disorders and healthy seniors, with a particular focus on remote/hybrid workshops, in which at least a part of the participants is connected remotely. Recommendations were gathered using a Delphi methodology. The expert panel (N = 18) included experts in the health, art and/or board games domains. They answered questions via two rounds of web-surveys, and then discussed the results in a plenary meeting. Some of the questions were also shared with the general public (N = 101). Both the experts and the general public suggested that organizing workshops in a hybrid format (some face-to-face sessions, some virtual session) is feasible and interesting for people with neurocognitive disorders. We reported guidelines on the overall structure of workshops, practical tips on how to organize remote workshops, and a SWOT analysis of the use of remote/hybrid workshops. The guidelines may be employed by clinicians to decide, based on their needs and constraints, what interventions and what kind of workshop format to employ, as well as by researcher to standardize procedures to assess the effectiveness of non-pharmacological treatments for people with neurocognitive disorders.

Living in rural area: A protective factor for a negative experience of the lockdown and the COVID-19 crisis in the oldest old population?

OBJECTIVES

Some factors influence the experience of the COVID-19 pandemic (health, loneliness, digital access...), but what about the living area? The objective was to compare between rural and urban areas, the psychological and social experiences of the older individuals with regard to the COVID-19 crisis during the first French lockdown.

METHODS

The sample included participants of three existing population-based cohorts on aging. Telephone interviews conducted by psychologists focused on the lockdown period. Data collected included living environment, professional assistance, social support, contacts with relatives, difficulties encountered, health, and knowledge and representations of the epidemic. The negative experience was defined by the presence of at least two of the following items: high anxiety symptomatology, depressive symptoms, worries or difficulties during the lockdown and insufficient social support.

RESULTS

The sample included 467 participants, aged on average 87.5 years (5.2), 58.9% were female and 47.1% lived in rural areas. Persons living in rural area had better social support, greater family presence, a less frequent feeling of imprisonment (OR = 0.60, 95 CI% = 0.36-0.99), 95% had a garden (vs. 56%), fewer depressive symptoms and lower anxiety scores, but also tended to lower comply with the health measures. Finally, they had an almost twofold lower risk of having a negative experience of the lockdown compared to their urban counterparts (OR = 0.55, 95% CI = 0.33-0.92, p = 0.0223).

CONCLUSIONS

The oldest old living in rural area experienced the first lockdown better than the urbans. Living conditions, with access to nature, a greater social support and family presence, could have contributed to these findings.

Measuring neuropsychiatric symptoms in patients with early cognitive decline using speech analysis

BACKGROUND

Certain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders.

METHODS

Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.

RESULTS

Different speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality-and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores.

CONCLUSIONS

Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.