Self-management for young people with mental health conditions

2011 ◽  
Vol 16 (10) ◽  
pp. 502-502
Author(s):  
Cathy McMahon
Keyword(s):  
Mental Health ◽  
Young People ◽  
Health Conditions ◽  
Self Management ◽  
Mental Health Conditions

scholarly journals Older Adult Peer Specialists’ Role in Reducing Loneliness Among People With Mental Health Conditions

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 662-662
Author(s):  
Karen Fortuna ◽  
George Mois ◽  
Jessica Brooks ◽  
Amanda Myers ◽  
Cynthia Bianco
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Real World ◽  
Smartphone Application ◽  
Interactive Effects ◽  
Health Conditions ◽  
Self Management ◽  
Potential Effectiveness ◽  
Mental Health Conditions ◽  
Management Intervention

Abstract PeerTECH is a peer-delivered and technology-support integrated medical and psychiatric self-management intervention developed by peers. A pre/post trial by our group has shown PeerTECH is associated with statistically significant improvements in self-efficacy for managing chronic disease and psychiatric self-management skills. This presentation will discuss the feasibility and potential effectiveness of using ecological momentary assessments (EMA) with older adults with mental health conditions to allow us to recognize early signs of loneliness and intervene as early as possible in real-world settings. EMA involves repeated sampling of an individual’s behaviors and experiences in real time, real-world environments on the smartphone application. Then, we will discuss the main and interactive effects of loneliness and factors linked to mortality. In conclusion, we will discuss potential effectiveness of PeerTECH with older adults with SMI.

scholarly journals Using the patient activation measure to examine the self-management support needs of a population of UK workers with long-term health conditions

2021 ◽  
pp. 174239532110434
Author(s):  
Sally Hemming ◽  
Fehmidah Munir
Keyword(s):  
Mental Health ◽  
Patient Activation ◽  
Health Conditions ◽  
Self Management ◽  
Support Needs ◽  
Management Support ◽  
Long Term Conditions ◽  
Mental Health Conditions ◽  
Patient Activation Measure

Objectives To examine differences in patient activation and self-management support needs in a population of UK workers with long-term health conditions. Methods Demographic, health and activation information were taken from the data of participants with long-term conditions, collected via an online cross-sectional survey of workers. The 13-item British patient activation measure measured workers knowledge, skills and confidence towards self-managing. Results Three hundred and seven workers with mental health, musculoskeletal and other conditions completed the patient activation measure. Mental health conditions were most prevalent (36.8%). Workers were higher activated, however workers with mental health conditions were significantly less activated ( p = 0.006). Differences in activation by condition severity and age were revealed. Discussion This study provides insight to the activation of UK workers with long-term conditions. Whilst workers with mental health conditions need more training and education to self-manage, workers are variably activated indicating broader support needs. There is a gap for workplace self-management support. The patient activation measure is used in healthcare to improve people’s self-management and should be considered to be included in the workplace, and could form part of interventions to support workers self-management. More rigorous studies, including the patient activation measure, are needed to identify the best approaches to identifying workers self-management support needs.

scholarly journals Methods and Measures Used to Evaluate Patient-Operated Mobile Health Interventions: Scoping Literature Review (Preprint)

2019 ◽  
Author(s):  
Meghan Bradway ◽  
Elia Gabarron ◽  
Monika Johansen ◽  
Paolo Zanaboni ◽  
Patricia Jardim ◽  
...  
Keyword(s):  
Mental Health ◽  
Mobile Health ◽  
Self Efficacy ◽  
Health Intervention ◽  
Noncommunicable Disease ◽  
Health Conditions ◽  
Self Management ◽  
Qualitative And Quantitative ◽  
Mental Health Conditions ◽  
Mhealth Apps

BACKGROUND Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. OBJECTIVE This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. METHODS A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. RESULTS A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). CONCLUSIONS This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice.

General Practitioners' management of young people with mental health conditions in Australia

2019 ◽  
Vol 14 (1) ◽  
pp. 124-129
Author(s):  
Michelle Kehoe ◽  
Toby Winton‐Brown ◽  
Stuart Lee ◽  
Liza Hopkins ◽  
Glenda Pedwell
Keyword(s):  
Mental Health ◽  
Young People ◽  
General Practitioners ◽  
Health Conditions ◽  
Mental Health Conditions

scholarly journals Effectiveness and impact of networked communication interventions in young people with mental health conditions: A rapid review

2018 ◽  
Vol 4 ◽  
pp. 205520761876220 ◽  
Author(s):  
Alice Verran ◽  
Ayesha Uddin ◽  
Rachel Court ◽  
Frances Taggart ◽  
Paul Sutcliffe ◽  
...  
Keyword(s):  
Mental Health ◽  
Young People ◽  
Mental Health Problems ◽  
Healthcare Providers ◽  
Evidence Base ◽  
Health Conditions ◽  
Rapid Review ◽  
Online Chat ◽  
Mental Health Conditions ◽  
Communication Interventions

Objective To describe the latest evidence of effectiveness and impact of networked communication interventions for young people with mental health conditions. Methods Searching five databases from 2009 onwards, we included studies of any design investigating two-way communication interventions for the treatment of young people (mean age 12–25) with a chronic mental health disorder. The data were synthesised using narrative summary. Results Six studies met the inclusion criteria, covering a range of mental health conditions (depression, psychosis, OCD). Interventions included an online chat room ( n = 2), videoconferencing ( n = 3) and telephone ( n = 1). Where studies compared two groups, equivalence or a statistically significant improvement in symptoms was observed compared to control. Views of patients and clinicians included impact on the patient-clinician interaction. Clinicians did not feel it hindered their diagnostic ability. Conclusion Networked communication technologies show promise in the treatment of young people with mental health problems but the current available evidence remains limited and the evidence base has not advanced much since the previous inception of this review in 2011. Practice implications Although the available research is generally positive, robust evidence relating to the provision of care for young persons via these technologies is lacking and healthcare providers should be mindful of this.

scholarly journals Employment of young people with mental health conditions: making it work

2020 ◽  
pp. 1-11
Author(s):  
Mythily Subramaniam ◽  
Yunjue Zhang ◽  
Shazana Shahwan ◽  
Janhavi Ajit Vaingankar ◽  
Pratika Satghare ◽  
...  
Keyword(s):  
Mental Health ◽  
Young People ◽  
Health Conditions ◽  
Mental Health Conditions

scholarly journals Involvement of community paediatricians in the care of children and young people with mental health difficulties in the UK: implications for case ascertainment by child and adolescent psychiatric, and paediatric surveillance systems

2021 ◽  
Vol 5 (1) ◽  
pp. e000713
Author(s):  
Hani F Ayyash ◽  
Michael Oladipo Ogundele ◽  
Richard M Lynn ◽  
Tanja-Sabine Schumm ◽  
Cornelius Ani
Keyword(s):  
Mental Health ◽  
Young People ◽  
Child Health ◽  
Child And Adolescent ◽  
Health Conditions ◽  
Emotional Difficulties ◽  
Children And Young People ◽  
Mental Health Conditions ◽  
Case Ascertainment ◽  
The Uk

ObjectiveTo ascertain the extent to which community paediatricians are involved in the care of children with mental health conditions in order to determine which difficulties are appropriate for single or joint surveillance by the British Paediatric Surveillance Unit (BPSU) and Child and Adolescent Psychiatry Surveillance System (CAPSS).DesignAn online survey of the 1120 members of the British Association of Community Child Health (BACCH) working in 169 Community Child Health (CCH) services in the UK.ResultsA total of 245 community paediatricians responded to the survey. This represents 22% of members of BACCH but likely to have covered many of the 169 CCH units because participants could respond on behalf of other members in their unit. The survey showed that children and young people (CYP) with neurodevelopmental conditions presented more frequently to paediatrics than to Child and Adolescent Mental Health Services (CAMHS). In addition, a sizeable proportion of CYP with emotional difficulties presented to paediatricians (eg, 29.5% for anxiety/obsessive compulsive disorder (OCD), and 12.8% for depression)—mainly due to difficulty with accessing CAMHS. More than half of the community paediatricians are involved in the care of CYP with anxiety and OCD, while 32.3% are involved in the care of those with depression.ConclusionThere is significant involvement of community paediatricians in the care of CYP with mental health conditions. Involvement is highest for neurodevelopmental conditions, but also significant for CYP with emotional difficulties. The implication of the findings for surveillance case ascertainment is that joint BPSU and CAPSS is recommended for surveillance studies of neurodevelopmental conditions. However, for emotional disorders, single or joint surveillance should be made based on the specific research question and the relative trade-offs between case ascertainment, and the additional cost and reporting burden of joint surveillance. Single CAPSS studies remain appropriate for psychosis and bipolar disorder.

scholarly journals Methods and Measures Used to Evaluate Patient-Operated Mobile Health Interventions: Scoping Literature Review

10.2196/16814 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e16814 ◽  
Author(s):  
Meghan Bradway ◽  
Elia Gabarron ◽  
Monika Johansen ◽  
Paolo Zanaboni ◽  
Patricia Jardim ◽  
...  
Keyword(s):  
Mental Health ◽  
Mobile Health ◽  
Self Efficacy ◽  
Health Intervention ◽  
Noncommunicable Disease ◽  
Health Conditions ◽  
Self Management ◽  
Qualitative And Quantitative ◽  
Mental Health Conditions ◽  
Mhealth Apps

Background Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. Objective This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. Methods A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. Results A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). Conclusions This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice.

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