Yolanda Lau

Objective: To compare the incidence of diabetes mellitus and its associations with hospitalisation and mortality in adults with multiple sclerosis (MS) and matched controls. Methods: Using English primary care data from the Clinical Practice Research Datalink, linked to hospital and mortality records, we matched adults with MS with up to 10 controls without MS by age, sex and practice. We excluded individuals with pre‐existing diabetes mellitus. Outcomes included all‐cause hospitalisation (number and duration) and mortality. We used Poisson, negative binomial, linear and Cox proportional hazards models, adjusting for demographic and socioeconomic factors, with interaction terms for ethnicity, deprivation and urbanity. Results: We included 47,247 individuals with MS and 458,951 matched controls. Over a mean follow‐up of 15.2 years, people with MS had a higher incidence of diabetes mellitus (adjusted incidence rate ratio = 1.16, 95% confidence interval 1.11–1.21). Among people with MS, incident diabetes mellitus was associated with higher hospitalisation rates (adjusted incidence rate ratio = 1.52, 1.42–1.64), longer hospitalisation duration (median 12 vs. 2 days) and increased mortality (adjusted hazard ratio = 1.41, 1.31–1.49). Similar patterns were observed among controls. The association between diabetes mellitus and increased hospitalisation was stronger in rural areas among those with MS and in White groups among controls. Interpretation: MS was associated with a higher incidence of diabetes mellitus, which was in turn associated with greater hospitalisation and mortality. These findings may support consideration of equitable screening, prevention and management of diabetes mellitus in people living with MS.

Background: Depressive and anxiety symptoms are prevalent among older adults and associated with increased dementia risk. In healthy older adults, higher step counts are associated with fewer depressive and anxiety symptoms; whether this holds in individuals with cognitive concerns (subjective cognitive decline [SCD] or mild cognitive impairment [MCI]) is unknown. In a randomised controlled trial, the 12-month APPLE-Tree group psychosocial lifestyle intervention produced small cognitive improvements but no change in step count. Objective: To test whether step count was associated with depressive and anxiety symptoms (cross-sectionally and over 24 months), and whether APPLE-Tree increased step count in participants with clinical anxiety or depression. Methods: We examined cross-sectional and longitudinal (12- and 24-month) associations between step count (two-week average from wrist-worn wearables) and depressive and anxiety symptoms (Hospital Anxiety and Depression Scale) using adjusted linear regressions, with a mediation analysis of self-perceived mobility. We also tested whether the intervention increased step count in those with baseline clinical anxiety or depression. Findings: We included 629 of 746 trial participants at baseline, of whom 376 contributed 12-month and 215 24-month data. At baseline, higher step counts were associated with fewer depressive symptoms (β = −0.11, 95% CI −0.17 to −0.05, p < 0.001) but, counter to our hypothesis, more anxiety symptoms (β = 0.12, 95% CI 0.06 to 0.19, p = 0.003). Over two years, change in step count was not associated with change in depressive or anxiety symptoms (all p ≥ 0.12). Self-perceived mobility problems mediated the association between step count and depressive but not anxiety symptoms. The intervention did not change step count in those with clinical anxiety or depression. Conclusions: This provides the first evidence in older adults with cognitive concerns that higher step counts are associated with fewer depressive but more anxiety symptoms. This may reflect heterogeneity of a population that includes those with prodromal dementia and cognitive health anxiety. Step count did not predict symptoms over time. Clinical implication: Step count may help distinguish anxiety and depressive symptoms in people presenting with cognitive concerns, or underlying reasons for cognitive concerns among those with functional cognitive disorders. KEY MESSAGES: What is already known on this topic – summarise the state of scientific knowledge on this subject before you did your study and why this study needed to be done Physical inactivity, depressive symptoms, and anxiety symptoms are linked to negative health outcomes and increased dementia risk. Existing research has shown that physical activity (e.g., walking) is associated with lower depressive and anxiety symptoms in healthy adults, and physical activity interventions are increasingly being employed to reduce these symptoms. Such interventions could offer benefit for individuals at risk of dementia; however, it remains unknown whether the relationship between physical activity and depressive and anxiety symptoms is the same in people with cognitive concerns, who may struggle to engage in planned physical activities. What this study adds – summarise what we now know as a result of this study that we did not know before This study extends existing research by showing that higher step counts were associated with fewer depressive symptoms in older adults at risk of developing dementia (i.e., with subjective cognitive decline [SCD] or mild cognitive impairment [MCI]). However, contrary to existing research, higher step counts were associated with more anxiety symptoms in this population. Self-perceived mobility problems fully mediated the association between step count and depressive symptoms, but did not mediate the association with anxiety symptoms, suggesting that the mechanisms linking step count with depressive and anxiety symptoms differ in older adults at risk of dementia. In longitudinal analyses, neither the intervention nor change in step count was associated with change in symptoms, indicating that step count may be a correlate of symptoms rather than a modifiable target in this population. How this study might affect research, practice or policy – summarise the implications of this study Walking interventions may be most appropriate for older adults with SCD or MCI experiencing depressive symptoms. Several studies have questioned whether among individuals with obsessional anxiety symptoms who have eating disorders, fitness trackers may reinforce worries. In people presenting with cognitive concerns without dementia, anxiety symptoms will have heterogenous aetiologies: worries about memory loss, as well as health anxieties that include fears of getting dementia. It is important to reflect on the impact of generic messaging on different groups, and perhaps to consider in further research the possibility that encouraging use of exercise trackers may compound these anxieties for a minority. For policymakers and practitioners, this underlines the importance of tailoring dementia prevention advice.

Importance: As new treatments increase quality and length of life in people with multiple sclerosis (MS), effective prevention and management of common comorbidities, including Diabetes Mellitus (DM), is increasingly important. Objective: To compare incidence of DM and its associations with hospitalisation and mortality in adults with MS and matched controls. Design: Using English primary care data from the Clinical Practice Research Datalink (CPRD), linked to Hospital Episode Statistics and national mortality records, we matched adults with MS diagnosed between 2000 and 2023, with up to ten controls without MS by age, sex, and practice. We excluded individuals with pre-existing DM, defined using diagnostic and management codes. Outcomes included all-cause hospitalisation (number and duration) and mortality. We used Poisson, negative binomial, linear, and Cox proportional hazards models, adjusting for demographic and socioeconomic factors, adding interaction terms to examine if ethnicity, deprivation, and urbanity were associated with outcomes. Results: We included 9,010 individuals with MS and 78,121 matched controls. Over a mean follow-up of 13.2 years, people with MS had over twice the incidence of DM compared with controls (adjusted incidence rate ratio [aIRR]=2.26, 95% CI: 1.96–2.61, p p p p p <0.001). The relationship between DM and increased hospitalisation was stronger in rural areas among those with MS and stronger in White groups among controls. Conclusions: People with MS are more likely to be diagnosed with DM, resulting in greater all-cause hospitalisation and all-cause mortality. This highlights the importance of equitable screening, prevention, and management of DM in people living with MS, with particular attention to geographical health inequalities. KEY POINTS: Question (one sentence): Is incident diabetes mellitus (DM) more common in individuals with MS than matched controls and is it associated with all-cause hospitalisation and mortality in this population? Findings (1-2 sentence): In this population-based matched cohort study of 87,131 adults (9,010 with MS), people with MS were twice as likely to be newly diagnosed with DM compared with controls. Among people with MS, a new DM diagnosis was associated with more and longer hospitalisations, and with higher all-cause mortality. Meaning (one sentence): Diabetes prevention, screening, and management has the potential to reduce hospitalisations and extend lives in people living with MS.

Objective: Antipsychotics, prescribed to manage behavioural and distress symptoms in dementia, are associated with increased mortality, so are often substituted with other medications. This review examines mortality risks associated with these alternatives. Method: We searched four electronic databases (January 2010 - October 2024), including randomised controlled trials, cohort and case-control studies involving people with dementia prescribed psychotropic medications (e.g., antidepressants, anxiolytics, mood stabilisers, sedatives, analgesics) other than antipsychotics. We assessed risk of bias using Cochrane Risk of Bias 2 and Newcastle-Ottawa Scale, and rated evidence certainty using GRADE criteria. Results: Thirty-six studies met inclusion criteria, most examining antidepressants (k=25). Only three were rated as being at low risk of bias. Low study quality and heterogeneity precluded meta-analyses. We found weak evidence of a trend in those prescribed antidepressants towards greater mortality risk in one high quality RCT, and lower risk in naturalistic cohort studies, relative to control conditions. Hypnotic, sedative or anxiolytic prescribing was not associated with greater all-cause mortality, relative to control conditions. Evidence for mood stabilisers/antiepileptics and opioids/analgesics was limited and inconsistent. Conclusions: Findings of a lack of clear association between prescribing and mortality should not be interpreted as evidence of safety. Future studies should examine impact of the drug types, dosages, treatment duration and adherence, to clarify potential within-class variation in mortality risk. Given the paucity of RCT evidence and practical challenges of powering trials for mortality, robust real-world studies are a priority. Pragmatic or registry-based randomised evaluations of mortality risks associated with treatment strategies may also be feasible.

Background: Digital biomarkers are gaining interest as proxy markers for mental health, as they enable passive and continuous data collection. However, the association between digital biomarkers of health and anxiety, both generalized anxiety disorder and anxiety symptoms, remains unknown. Objective: This systematic review and meta-analysis examined the association between digital biomarkers of health obtained from wrist-worn wearables and anxiety in adults. Methods: Systematic literature searches were conducted across 6 databases, including unpublished gray literature. The final search was done on September 21, 2025. Cross-sectional or longitudinal studies investigating the association between digital biomarkers from wrist-worn wearables and anxiety were eligible. Studies using inferential statistics or machine learning methods were both eligible. Studies were excluded if participants received diagnoses of neurodegenerative disorders or physical health conditions. Two risk-of-bias tools were used: the National Heart, Lung, and Blood Institute assessment tool for inferential statistical studies, and the modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 for machine learning studies. Whenever possible, effect sizes were combined across studies, for each digital biomarker of health separately, using random-effects meta-analyses. Sensitivity analyses were performed to assess whether results differed according to anxiety type (state or trait) and age group. Otherwise, studies were synthesized narratively. Results: A total of 44 studies from 42 articles were eligible. Among these, 36 studies used inferential statistical approaches for analysis (21 reporting sleep characteristics, 8 reporting physical activity, 2 reporting heart rate variability, and 5 reporting more than 1 type), and 8 studies used machine learning approaches. Sample size ranged from 17 to 170,320. Meta-analyses on 4 sleep metrics found no associations: sleep efficiency (Fisher z=–0.07, 95% CI –0.14 to 0.002; P=.06; PI –0.19 to 0.05), wake after sleep onset (Fisher z=0.13, 95% CI –0.04 to 0.30; P=.11; PI –0.15 to 0.41), total sleep time (Fisher z=0.009, 95% CI –0.01 to 0.03; P=.28; PI –0.02 to 0.03), and sleep onset latency (Fisher z=0.04, 95% CI –0.07 to 0.15; P=.08; PI –0.19 to 0.27). Qualitative syntheses revealed that lower physical activity levels and higher heart rate were associated with greater anxiety symptoms. Machine learning studies using wrist-worn wearable data alone showed varied performance, with predictive performance improving when wearable data were combined with other data sources. Conclusions: This is the first review to synthesize evidence from inferential statistical (mostly fair quality) and machine learning studies examining association between wearable-derived digital biomarkers and anxiety. Meta-analyses found no associations between sleep metrics and anxiety. Although based on limited studies, lower physical activity levels and elevated heart rate were associated with greater anxiety symptoms. Digital biomarkers may be more useful when integrated with other data sources (eg, self-report and clinical data) rather than used as stand-alone screening tools. Trial Registration: PROSPERO CRD42023409995; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023409995

BACKGROUND Digital biomarkers are gaining interest as proxy markers for mental health, as they enable passive and continuous data collection. However, the association between digital biomarkers of health and anxiety, both generalized anxiety disorder and anxiety symptoms, remains unknown. OBJECTIVE This systematic review and meta-analysis examined the association between digital biomarkers of health obtained from wrist-worn wearables and anxiety in adults. METHODS Systematic literature searches were conducted across 6 databases, including unpublished gray literature. The final search was done on September 21, 2025. Cross-sectional or longitudinal studies investigating the association between digital biomarkers from wrist-worn wearables and anxiety were eligible. Studies using inferential statistics or machine learning methods were both eligible. Studies were excluded if participants received diagnoses of neurodegenerative disorders or physical health conditions. Two risk-of-bias tools were used: the National Heart, Lung, and Blood Institute assessment tool for inferential statistical studies, and the modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 for machine learning studies. Whenever possible, effect sizes were combined across studies, for each digital biomarker of health separately, using random-effects meta-analyses. Sensitivity analyses were performed to assess whether results differed according to anxiety type (state or trait) and age group. Otherwise, studies were synthesized narratively. RESULTS A total of 44 studies from 42 articles were eligible. Among these, 36 studies used inferential statistical approaches for analysis (21 reporting sleep characteristics, 8 reporting physical activity, 2 reporting heart rate variability, and 5 reporting more than 1 type), and 8 studies used machine learning approaches. Sample size ranged from 17 to 170,320. Meta-analyses on 4 sleep metrics found no associations: sleep efficiency (Fisher z=–0.07, 95% CI –0.14 to 0.002; P=.06; PI –0.19 to 0.05), wake after sleep onset (Fisher z=0.13, 95% CI –0.04 to 0.30; P=.11; PI –0.15 to 0.41), total sleep time (Fisher z=0.009, 95% CI –0.01 to 0.03; P=.28; PI –0.02 to 0.03), and sleep onset latency (Fisher z=0.04, 95% CI –0.07 to 0.15; P=.08; PI –0.19 to 0.27). Qualitative syntheses revealed that lower physical activity levels and higher heart rate were associated with greater anxiety symptoms. Machine learning studies using wrist-worn wearable data alone showed varied performance, with predictive performance improving when wearable data were combined with other data sources. CONCLUSIONS This is the first review to synthesize evidence from inferential statistical (mostly fair quality) and machine learning studies examining association between wearable-derived digital biomarkers and anxiety. Meta-analyses found no associations between sleep metrics and anxiety. Although based on limited studies, lower physical activity levels and elevated heart rate were associated with greater anxiety symptoms. Digital biomarkers may be more useful when integrated with other data sources (eg, self-report and clinical data) rather than used as stand-alone screening tools. CLINICALTRIAL PROSPERO CRD42023409995; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023409995

Question: What is the current evidence base for the association between digital biomarkers from wrist-worn wearables, loneliness and social isolation in adults? Study selection and analysis: We systematically searched six databases from inception to 24 ^th September, 2024. We narratively synthesised findings and pooled effect sizes using random-effects meta-analyses where possible. Findings: We included 14 studies from 12 articles (12 assessing loneliness, two assessing social isolation). Eight studies used sleep metrics, four used physical activity metrics, and two studies used machine learning approaches. Three meta-analyses were conducted: worse sleep efficiency (SE), but not total sleep time or sleep onset latency, was associated with higher loneliness (Fisher’s z = −0.20, 95% CI −0.34 to −0.06, p = 0.006). Two studies examined wake after sleep onset (WASO), and found longer periods of WASO were associated with higher loneliness. These findings on loneliness were echoed in the study examining social isolation. One study found that lower total physical activity was associated with higher levels of loneliness and social isolation, while other activity intensities showed mixed evidence. Machine learning studies demonstrated high accuracy in predicting loneliness, though models using digital biomarkers from smartphones provided better accuracy. Conclusions: Worse SE, more WASO, and lower total physical activity were associated with loneliness and social isolation, particularly in middle- and older-age. Digital biomarker-based machine learning studies are sparse but show potential in predicting loneliness. Leveraging digital biomarkers as proxy markers of loneliness and social isolation could facilitate early detection of these conditions. Key messages of the articles: What is already known on this topic: Loneliness and social isolation are linked to negative health outcomes, including increased dementia risk. Digital biomarkers have shown potential in detecting mental health conditions and symptoms, but no systematic review has explored their association with loneliness and social isolation. What this study adds: This review identified 14 studies examining the association between digital biomarkers and loneliness or social isolation. Worse sleep efficiency, more wake after sleep onset, and lower physical activity were associated with higher levels of loneliness and social isolation. How this study might affect research, practice or policy: Digital biomarkers could support the early detection of loneliness and social isolation, enabling timely intervention.