Motahare Yadegarfar

Bio: Motahare Yadegarfar is an academic researcher from University of Leicester. The author has contributed to research in topics: Mood disorders & Dementia. The author has an hindex of 5, co-authored 9 publications receiving 903 citations. Previous affiliations of Motahare Yadegarfar include Leicester Royal Infirmary & University of Warwick.

Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: meta-analysis

Abstract: Objective: To investigate whether people with subjective memory complaints (SMC) but no objective deficits are at increased risk of developing mild cognitive impairment (MCI) and dementia. Method: Major electronic databases were searched till 03/2014, and a meta-analysis was conducted using inception cohort studies. Results: Across 28 studies, there were 29 723 unique individuals (14 714 with SMC and 15 009 without SMC) (mean 71.6 years) followed on average for 4.8 years through to dementia. The annual conversion rate (ACR) of SMC to dementia was 2.33% (95% CI = 1.93%–2.78%) a relative risk (RR) of 2.07 (95% CI = 1.76–2.44) compared with those without SMC (n = 15 009). From 11 studies the ACR of developing MCI was 6.67% (95% CI = 4.70–8.95%). In long-term studies over 4 years, 14.1% (9.67–19.1%) of people with SMC developed dementia and 26.6% (95% CI = 5.3–39.7) went on to develop MCI. The ACR from SMC to dementia and MCI were comparable in community and non-community settings. Conclusion: Older people with SMC but no objective complaints are twice as likely to develop dementia as individuals without SMC. Approximately 2.3% and 6.6% of older people with SMC will progress to dementia and MCI per year.

Case finding and screening clinical utility of the Patient Health Questionnaire (PHQ-9 and PHQ-2) for depression in primary care: a diagnostic meta-analysis of 40 studies.

Abstract: Background The Patient Health Questionnaire (PHQ) is the most commonly used measure to screen for depression in primary care but there is still lack of clarity about its accuracy and optimal scoring method. Aims To determine via meta-analysis the diagnostic accuracy of the PHQ-9-linear, PHQ-9-algorithm and PHQ-2 questions to detect major depressive disorder (MDD) among adults. Method We systematically searched major electronic databases from inception until June 2015. Articles were included that reported the accuracy of PHQ-9 or PHQ-2 questions for diagnosing MDD in primary care defined according to standard classification systems. We carried out a meta-analysis, meta-regression, moderator and sensitivity analysis. Results Overall, 26 publications reporting on 40 individual studies were included representing 26 902 people (median 502, s.d.=693.7) including 14 760 unique adults of whom 14.3% had MDD. The methodological quality of the included articles was acceptable. The meta-analytic area under the receiver operating characteristic curve of the PHQ-9-linear and the PHQ-2 was significantly higher than the PHQ-9-algorithm, a difference that was maintained in head-to-head meta-analysis of studies. Our best estimates of sensitivity and specificity were 81.3% (95% CI 71.6–89.3) and 85.3% (95% CI 81.0–89.1), 56.8% (95% CI 41.2–71.8) and 93.3% (95% CI 87.5–97.3) and 89.3% (95% CI 81.5–95.1) and 75.9% (95% CI 70.1–81.3) for the PHQ-9-linear, PHQ-9-algorithm and PHQ-2 respectively. For case finding (ruling in a diagnosis), none of the methods were suitable but for screening (ruling out non-cases), all methods were encouraging with good clinical utility, although the cut-off threshold must be carefully chosen. Conclusions The PHQ can be used as an initial first step assessment in primary care and the PHQ-2 is adequate for this purpose with good acceptability. However, neither the PHQ-2 nor the PHQ-9 can be used to confirm a clinical diagnosis (case finding). Declaration of interest None. Copyright and usage © The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.

Breast cancer screening in women with mental illness: comparative meta-analysis of mammography uptake

Abstract: Background There is a higher mortality rate due to cancer in people with mental illness and previous work suggests suboptimal medical care in this population. It remains unclear if this extends to breast cancer population screening. Aims To conduct a systematic review and meta-analysis to establish if women with a mental health condition are less likely to receive mammography screening compared with those without mental ill health. Method Major electronic databases were searched from inception until February 2014. We calculated odds ratios (OR) with a random effects meta-analysis comparing mammography screening rates among women with and without a mental illness. Results were stratified according to primary diagnosis including any mental illness, mood disorders, depression, severe mental illness (SMI), distress and anxiety. Results We identified 24 publications reporting breast cancer screening practices in women with mental illness ( n = 715 705). An additional 5 studies investigating screening for those with distress ( n = 21 491) but no diagnosis of mental disorder were identified. The pooled meta-analysis showed significantly reduced rates of mammography screening in women with mental illness (OR = 0.71, 95% CI 0.66-0.77), mood disorders (OR = 0.83, 95% CI 0.76-0.90) and particularly SMI (OR = 0.54, 95% CI 0.45-0.65). No disparity was evident among women with distress alone. Conclusions Rates of mammography screening are lower in women with mental illness, particularly women with SMI, and this is not explained by the presence of emotional distress. Disparities in medical care due to mental illness clearly extend into preventive population screening.

Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria.

Abstract: During the past decade, a conceptual shift occurred in the field of Alzheimer's disease (AD) considering the disease as a continuum. Thanks to evolving biomarker research and substantial discoveries, it is now possible to identify the disease even at the preclinical stage before the occurrence of the first clinical symptoms. This preclinical stage of AD has become a major research focus as the field postulates that early intervention may offer the best chance of therapeutic success. To date, very little evidence is established on this "silent" stage of the disease. A clarification is needed about the definitions and lexicon, the limits, the natural history, the markers of progression, and the ethical consequence of detecting the disease at this asymptomatic stage. This article is aimed at addressing all the different issues by providing for each of them an updated review of the literature and evidence, with practical recommendations.

Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis

Abstract: Objective To determine the accuracy of the Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression. Design Individual participant data meta-analysis. Data sources Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, and Web of Science (January 2000-February 2015). Inclusion criteria Eligible studies compared PHQ-9 scores with major depression diagnoses from validated diagnostic interviews. Primary study data and study level data extracted from primary reports were synthesized. For PHQ-9 cut-off scores 5-15, bivariate random effects meta-analysis was used to estimate pooled sensitivity and specificity, separately, among studies that used semistructured diagnostic interviews, which are designed for administration by clinicians; fully structured interviews, which are designed for lay administration; and the Mini International Neuropsychiatric (MINI) diagnostic interviews, a brief fully structured interview. Sensitivity and specificity were examined among participant subgroups and, separately, using meta-regression, considering all subgroup variables in a single model. Results Data were obtained for 58 of 72 eligible studies (total n=17 357; major depression cases n=2312). Combined sensitivity and specificity was maximized at a cut-off score of 10 or above among studies using a semistructured interview (29 studies, 6725 participants; sensitivity 0.88, 95% confidence interval 0.83 to 0.92; specificity 0.85, 0.82 to 0.88). Across cut-off scores 5-15, sensitivity with semistructured interviews was 5-22% higher than for fully structured interviews (MINI excluded; 14 studies, 7680 participants) and 2-15% higher than for the MINI (15 studies, 2952 participants). Specificity was similar across diagnostic interviews. The PHQ-9 seems to be similarly sensitive but may be less specific for younger patients than for older patients; a cut-off score of 10 or above can be used regardless of age.. Conclusions PHQ-9 sensitivity compared with semistructured diagnostic interviews was greater than in previous conventional meta-analyses that combined reference standards. A cut-off score of 10 or above maximized combined sensitivity and specificity overall and for subgroups. Registration PROSPERO CRD42014010673.

The characterisation of subjective cognitive decline

Abstract: A growing awareness about brain health and Alzheimer's disease in the general population is leading to an increasing number of cognitively unimpaired individuals, who are concerned that they have reduced cognitive function, to approach the medical system for help. The term subjective cognitive decline (SCD) was conceived in 2014 to describe this condition. Epidemiological data provide evidence that the risk for mild cognitive impairment and dementia is increased in individuals with SCD. However, the majority of individuals with SCD will not show progressive cognitive decline. An individually tailored diagnostic process might be reasonable to identify or exclude underlying medical conditions in an individual with SCD who actively seeks medical help. An increasing number of studies are investigating the link between SCD and the very early stages of Alzheimer's disease and other neurodegenerative diseases.

Implementation of subjective cognitive decline criteria in research studies.

Abstract: Introduction Subjective cognitive decline (SCD) manifesting before clinical impairment could serve as a target population for early intervention trials in Alzheimer's disease (AD). A working group, the Subjective Cognitive Decline Initiative (SCD-I), published SCD research criteria in the context of preclinical AD. To successfully apply them, a number of issues regarding assessment and implementation of SCD needed to be addressed. Methods Members of the SCD-I met to identify and agree on topics relevant to SCD criteria operationalization in research settings. Initial ideas and recommendations were discussed with other SCD-I working group members and modified accordingly. Results Topics included SCD inclusion and exclusion criteria, together with the informant's role in defining SCD presence and the impact of demographic factors. Discussion Recommendations for the operationalization of SCD in differing research settings, with the aim of harmonization of SCD measurement across studies are proposed, to enhance comparability and generalizability across studies.

An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study

Abstract: Background: A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. Objective: This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. Methods: In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. Results: The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7% of user-provided feedback responses found the app experience helpful and encouraging. Conclusions: The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods.