scholarly journals Monitoring beliefs and physiological measures in students at risk for COVID-19 using wearable sensors and smartphone technology: Protocol for a mobile health study (Preprint)

2021 ◽  
Author(s):  
Christine Cislo ◽  
Caroline Clingan ◽  
Kristen Gilley ◽  
Michelle Rozwadowski ◽  
Izzy Gainsburg ◽  
...  
Keyword(s):  
Mental Health ◽  
At Risk ◽  
Mobile Health ◽  
Wearable Sensors ◽  
Well Being ◽  
Student Health ◽  
Wearable Sensor ◽  
Students At Risk ◽  
College Student Population ◽  
Health And Well Being

BACKGROUND The COVID-19 pandemic has impacted lives significantly and greatly affected an already vulnerable population, college students, in relation to mental health and public safety. Social distancing and isolation have brought about challenges to student’s mental health. Mobile health apps and wearable sensors may help to monitor students at risk for COVID-19 and support their mental well-being. OBJECTIVE Through the use of a wearable sensor and smartphone-based survey completion, this study aimed to monitor students at risk for COVID-19. METHODS We conducted a prospective study of students, undergraduate and graduate, at a public university in the Midwest. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal mobile devices (Android or iOS). Subjects consented to provide up to 10 saliva samples during the study period. Surveys were administered through the Roadmap 2.0 app at five timepoints - at baseline, 1-month later, 2-months later, 3-months later, and at study completion. The surveys gathered information regarding demographics, COVID-19 diagnoses and symptoms, and mental health resilience, with the aim of documenting the impact of COVID-19 on the college student population. RESULTS This study enrolled 2158 college students between September 2020 and January 2021. Subjects are currently being followed on-study for one academic year. Data collection and analysis are ongoing. CONCLUSIONS This study examined student health and well-being during the COVID-19 pandemic. While data collection and analyses are ongoing, the study will assess the feasibility of wearable sensor use and survey completion in a college student population, which may inform the role of our mobile health tools on student health and well-being. Finally, using wearable sensor data, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data towards the development of a model for the early prediction and detection of COVID-19. CLINICALTRIAL ClinicalTrials.gov NCT04766788

Using the WHO–5 Well-Being Index to Identify College Students at Risk for Mental Health Problems

2017 ◽  
Vol 58 (1) ◽  
pp. 113-117 ◽  
Author(s):  
Andrew Downs ◽  
Laura A. Boucher ◽  
Duncan G. Campbell ◽  
Anita Polyakov
Keyword(s):  
Mental Health ◽  
College Students ◽  
At Risk ◽  
Mental Health Problems ◽  
Well Being ◽  
Health Problems ◽  
Students At Risk

scholarly journals The health and well-being effects of drought: assessing multi-stakeholder perspectives through narratives from the UK

2020 ◽  
Vol 163 (4) ◽  
pp. 2073-2095
Author(s):  
Kimberly Bryan ◽  
Sarah Ward ◽  
Liz Roberts ◽  
Mathew P. White ◽  
Owen Landeg ◽  
...  
Keyword(s):  
Climate Change ◽  
Mental Health ◽  
At Risk ◽  
Health Effects ◽  
Well Being ◽  
Risk Groups ◽  
Potential Health ◽  
Positive Effects ◽  
The Uk ◽  
Health And Well Being

AbstractThe global literature on drought and health highlights a variety of health effects for people in developing countries where certain prevailing social, economic and environmental conditions increase their vulnerability especially with climate change. Despite increased focus on climate change, relatively less is known about the health-drought impacts in the developed country context. In the UK, where climate change–related risk of water shortages has been identified as a key area for action, there is need for better understanding of drought-health linkages. This paper assesses people’s narratives of drought on health and well-being in the UK using a source-receptor-impact framing. Stakeholder narratives indicate that drought can present perceived health and well-being effects through reduced water quantity, water quality, compromised hygiene and sanitation, food security, and air quality. Heatwave associated with drought was also identified as a source of health effects through heat and wildfire, and drought-related vectors. Drought was viewed as potentially attributing both negative and positive effects for physical and mental health, with emphasis on mental health. Health impacts were often complex and cross-sectoral in nature indicating the need for a management approach across several sectors that targets drought and health in risk assessment and adaptation planning processes. Two recurring themes in the UK narratives were the health consequences of drought for ‘at-risk’ groups and the need to target them, and that drought in a changing climate presented potential health implications for at-risk groups.

scholarly journals Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (Preprint)

2017 ◽  
Author(s):  
Akane Sano ◽  
Sara Taylor ◽  
Andrew W McHill ◽  
Andrew JK Phillips ◽  
Laura K Barger ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Wearable Sensors ◽  
High Stress ◽  
Well Being ◽  
Poor Mental Health ◽  
Wearable Sensor ◽  
Multimodal Data

BACKGROUND Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. OBJECTIVE We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. METHODS We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. RESULTS We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. CONCLUSIONS New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.

scholarly journals The impact on the mental health and well-being of unpaid carers affected by social distancing, self-isolation and shielding during the COVID 19 pandemic in England – a systematic review

2021 ◽  
Author(s):  
Tabo Akafekwa ◽  
Elizabeth Dalgarno ◽  
Arpana Verma
Keyword(s):  
Mental Health ◽  
At Risk ◽  
Well Being ◽  
Poor Mental Health ◽  
List Type ◽  
Digital Platforms ◽  
Health And Wellbeing ◽  
The People ◽  
The Impact ◽  
Health And Well Being

AbstractObjectiveThis study explores the impact of the COVID 19 lockdown measures on the mental health and well-being of unpaid carers, who make up the largest number of the carer population in England.Study designA systematic review research protocol was designed and used to conduct the review along with the Enhancing Transparency in Reporting the synthesis of Qualitative Research - ENTREQ statement [43]. Pre-determined inclusion and exclusion criteria were used. EndNote X9 reference management was used and the search process was represented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [76]. Appraisal of the included research was carried out using the Critical Appraisal Skills Programme (CASP) [57]. Line by line coding was done using inductive thematic synthesis and EPPI Reviewer 4 software [60].ResultsFour themes emerged; immediate worries or fears, adapting to change, post pandemic fears and use of technology.ConclusionThe measures put in place during the first lockdown period have had detrimental impacts on unpaid carers, putting them at greater risk of burnout. However, use of digital platforms could have a positive impact on well-being. Recommendations for further research are provided.What is new?Key findings?Discontinued or reduced access to activities and services during the first lockdown during the pandemic has had a negative impact on both people who require care and their carers.Carers prioritise the mental health and wellbeing needs of the people they care for over their own.Further qualitative research from different groups of carers would be useful to gain a deeper understanding of the impact of the COVID 19 pandemic measures on unpaid carers.Use of digital technology and digital platforms may be useful tools for carers both during the pandemic and after.What this adds to what is known?There have been very few qualitative studies on the impact of the COVID 19 pandemic on the mental health and wellbeing of unpaid carers, this review has synthesised their findings and will contribute to future research.Unpaid carers are known to be at risk of poor mental health and wellbeing outcomes, this review demonstrates that they are even more at risk due to the increased reliance on them during the pandemic.What is this implication and what should change?There is limited qualitative data available from a range of different groups of carers for example, spouse carers, parent carers, carers of people who have specific needs or conditions. Therefore, purposeful sample research to determine the needs of groups of carers during the COVID 19 pandemic could be valuable.Unpaid carers who do not have appropriate support are more at risk of poor mental health and wellbeing outcomes. During the pandemic services have had to adapt to the various rules implemented. Digital adaptations to the provision of support to both carers and the people they care for could be beneficial both during and after the pandemic.

scholarly journals The “Supporting Kids, Avoiding Problems” (SKIP) study: relationships between school exclusion, psychopathology, development and attainment – a case control study

2016 ◽  
Vol 11 (2) ◽  
pp. 91-110 ◽  
Author(s):  
Claire Parker ◽  
Ruth Marlow ◽  
Marc Kastner ◽  
Felix May ◽  
Oana Mitrofan ◽  
...  
Keyword(s):  
Mental Health ◽  
At Risk ◽  
Normative Data ◽  
Well Being ◽  
Case Control ◽  
Content Type ◽  
Control Approach ◽  
Number Of Children ◽  
Behavioural Difficulties ◽  
Health And Well Being

Purpose – The purpose of this paper is to explore the association between children who are at risk of being or who have been excluded from school between the ages of 4 and 12 years and the role of psychopathology, development and attainment. Design/methodology/approach – A case-control approach was conducted. Cases were children who had been excluded from school compared to those who had no reported exclusions and normative data where possible. A range of measures were used to collect information from the parent, child and teacher on areas covering the child’s mental health and well-being. Findings – The findings showed the number of difficulties faced by children who are at risk of being or who have been excluded from school compared to gender- and age-matched controls and normative data increased. Behavioural difficulties were apparent in the majority of the cases and an alarming number of children reported self-harm. Interestingly nearly all the cases had recognised needs, but not all of them were accessing appropriate services. Practical implications – There have been a number of changes regarding the identification and support of children’s mental health and well-being. This study highlights gaps in resources and provision, particularly around behavioural difficulties for children who are presenting as not coping in school. Originality/value – The findings from the SKIP study indicate the complexities and compounded difficulties faced by children who are experiencing exclusion from school. By implementing a systematic group of assessments the study was able to identify these complexities of need across a vulnerable group of children.

scholarly journals Assessment of Population Well-Being With the Mental Health Quotient (MHQ): Development and Usability Study (Preprint)

2020 ◽  
Author(s):  
Jennifer Jane Newson ◽  
Tara C Thiagarajan
Keyword(s):  
Mental Health ◽  
At Risk ◽  
General Population ◽  
Diagnostic Criteria ◽  
Well Being ◽  
Assessment Tools ◽  
Web Based ◽  
Health Symptoms ◽  
Dsm 5 ◽  
Health And Well Being

BACKGROUND Existing mental health assessment tools provide an incomplete picture of symptom experience and create ambiguity, bias, and inconsistency in mental health outcomes. Furthermore, by focusing on disorders and dysfunction, they do not allow a view of mental health and well-being across a general population. OBJECTIVE This study aims to demonstrate the outcomes and validity of a new web-based assessment tool called the Mental Health Quotient (MHQ), which is designed for the general population. The MHQ covers the complete breadth of clinical mental health symptoms and also captures healthy mental functioning to provide a complete profile of an individual’s mental health from clinical to thriving. METHODS The MHQ was developed based on the coding of symptoms assessed in 126 existing Diagnostic and Statistical Manual of Mental Disorders (DSM)–based psychiatric assessment tools as well as neuroscientific criteria laid out by Research Domain Criteria to arrive at a comprehensive set of semantically distinct mental health symptoms and attributes. These were formulated into questions on a 9-point scale with both positive and negative dimensions and developed into a web-based tool that takes approximately 14 min to complete. As its output, the assessment provides overall MHQ scores as well as subscores for 6 categories of mental health that distinguish clinical and at-risk groups from healthy populations based on a nonlinear scoring algorithm. MHQ items were also mapped to the DSM fifth edition (DSM-5), and clinical diagnostic criteria for 10 disorders were applied to the MHQ outcomes to cross-validate scores labeled at-risk and clinical. Initial data were collected from 1665 adult respondents to test the tool. RESULTS Scores in the normal healthy range spanned from 0 to 200 for the overall MHQ, with an average score of approximately 100 (SD 45), and from 0 to 100 with average scores between 48 (SD 21) and 55 (SD 22) for subscores in each of the 6 mental health subcategories. Overall, 2.46% (41/1665) and 13.09% (218/1665) of respondents were classified as clinical and at-risk, respectively, with negative scores. Validation against DSM-5 diagnostic criteria showed that 95% (39/41) of those designated clinical were positive for at least one DSM-5–based disorder, whereas only 1.14% (16/1406) of those with a positive MHQ score met the diagnostic criteria for a mental health disorder. CONCLUSIONS The MHQ provides a fast, easy, and comprehensive way to assess population mental health and well-being; identify at-risk individuals and subgroups; and provide diagnosis-relevant information across 10 disorders.

scholarly journals Assessment of Population Well-Being With the Mental Health Quotient (MHQ): Development and Usability Study

10.2196/17935 ◽  
2020 ◽  
Vol 7 (7) ◽  
pp. e17935 ◽  
Author(s):  
Jennifer Jane Newson ◽  
Tara C Thiagarajan
Keyword(s):  
Mental Health ◽  
At Risk ◽  
General Population ◽  
Diagnostic Criteria ◽  
Well Being ◽  
Assessment Tools ◽  
Web Based ◽  
Health Symptoms ◽  
Dsm 5 ◽  
Health And Well Being

Background Existing mental health assessment tools provide an incomplete picture of symptom experience and create ambiguity, bias, and inconsistency in mental health outcomes. Furthermore, by focusing on disorders and dysfunction, they do not allow a view of mental health and well-being across a general population. Objective This study aims to demonstrate the outcomes and validity of a new web-based assessment tool called the Mental Health Quotient (MHQ), which is designed for the general population. The MHQ covers the complete breadth of clinical mental health symptoms and also captures healthy mental functioning to provide a complete profile of an individual’s mental health from clinical to thriving. Methods The MHQ was developed based on the coding of symptoms assessed in 126 existing Diagnostic and Statistical Manual of Mental Disorders (DSM)–based psychiatric assessment tools as well as neuroscientific criteria laid out by Research Domain Criteria to arrive at a comprehensive set of semantically distinct mental health symptoms and attributes. These were formulated into questions on a 9-point scale with both positive and negative dimensions and developed into a web-based tool that takes approximately 14 min to complete. As its output, the assessment provides overall MHQ scores as well as subscores for 6 categories of mental health that distinguish clinical and at-risk groups from healthy populations based on a nonlinear scoring algorithm. MHQ items were also mapped to the DSM fifth edition (DSM-5), and clinical diagnostic criteria for 10 disorders were applied to the MHQ outcomes to cross-validate scores labeled at-risk and clinical. Initial data were collected from 1665 adult respondents to test the tool. Results Scores in the normal healthy range spanned from 0 to 200 for the overall MHQ, with an average score of approximately 100 (SD 45), and from 0 to 100 with average scores between 48 (SD 21) and 55 (SD 22) for subscores in each of the 6 mental health subcategories. Overall, 2.46% (41/1665) and 13.09% (218/1665) of respondents were classified as clinical and at-risk, respectively, with negative scores. Validation against DSM-5 diagnostic criteria showed that 95% (39/41) of those designated clinical were positive for at least one DSM-5–based disorder, whereas only 1.14% (16/1406) of those with a positive MHQ score met the diagnostic criteria for a mental health disorder. Conclusions The MHQ provides a fast, easy, and comprehensive way to assess population mental health and well-being; identify at-risk individuals and subgroups; and provide diagnosis-relevant information across 10 disorders.

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