Mobile Health (mHealth) Apps for Self-Management of Depression or Anxiety: A Cross-Sectional Analysis (Preprint)

2021 ◽  
Author(s):  
Renee Robinson ◽  
Radhika Narsinghani ◽  
Elaine Nguyen
Keyword(s):  
Mental Health ◽  
Mobile Health ◽  
Access To Health Care ◽  
Management Program ◽  
Self Management ◽  
Management Support ◽  
Depression And Anxiety ◽  
User Friendliness ◽  
Cross Sectional ◽  
Mhealth Apps

BACKGROUND Depression and anxiety are common mental health disorders. Untreated or unmanaged depression and anxiety can lead to physical and/or behavioral health concerns. Many people suffering from depression and/or anxiety have inadequate access to health care and supports. Evidence supports that mobile health (mHealth) applications (apps) can be beneficial in the management of chronic conditions. OBJECTIVE Compare consumer-directed mobile-health applications (mHealth apps) available for self-management of depression and/or anxiety. METHODS A systematic review of 93,849 consumer-apps was conducted using a 3-step inclusion-criteria. Step-one: available in English, downloadable, and aligned with established self-management program components. Step-two: defined depression/anxiety, described symptoms, and discussed greater than 2-management techniques. Step-three: screened for user-friendliness and self-management components (n=10). Apps were assessed for readability and validity. RESULTS Seventy-percent of mHealth apps incorporated 4-major self-management components. Eighty-percent of apps described at least three DSM-5 symptoms. Thirty- three percent of apps were 5-grade-levels higher than general US comprehension estimates. Only 40% of reviewed apps provided evidence-based self-management support and only 20% were affiliated with an accredited organization. CONCLUSIONS mHealth apps have the potential to reduce barriers to access to mental health treatment. Further research is necessary to understand how pharmacists can better support patient self-management of depression/anxiety with mHealth apps.

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.

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.

scholarly journals Results from an 18 country cross-sectional study examining experiences of nature for people with common mental health disorders

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Michelle Tester-Jones ◽  
Mathew P. White ◽  
Lewis R. Elliott ◽  
Netta Weinstein ◽  
James Grellier ◽  
...  
Keyword(s):  
Mental Health ◽  
Intrinsic Motivation ◽  
Mental Health Disorders ◽  
Cross Sectional Study ◽  
Self Management ◽  
Natural Environments ◽  
Depression And Anxiety ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Disorders

Abstract Exposure to natural environments is associated with a lower risk of common mental health disorders (CMDs), such as depression and anxiety, but we know little about nature-related motivations, practices and experiences of those already experiencing CMDs. We used data from an 18-country survey to explore these issues (n = 18,838), taking self-reported doctor-prescribed medication for depression and/or anxiety as an indicator of a CMD (n = 2698, 14%). Intrinsic motivation for visiting nature was high for all, though slightly lower for those with CMDs. Most individuals with a CMD reported visiting nature ≥ once a week. Although perceived social pressure to visit nature was associated with higher visit likelihood, it was also associated with lower intrinsic motivation, lower visit happiness and higher visit anxiety. Individuals with CMDs seem to be using nature for self-management, but ‘green prescription’ programmes need to be sensitive, and avoid undermining intrinsic motivation and nature-based experiences.

scholarly journals The Use of Mobile Health to Deliver Self-Management Support to Young People With Type 1 Diabetes: A Cross-Sectional Survey

JMIR Diabetes ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. e4 ◽  
Author(s):  
Rosie Dobson ◽  
Robyn Whittaker ◽  
Rinki Murphy ◽  
Manish Khanolkar ◽  
Steven Miller ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Young People ◽  
Mobile Health ◽  
Self Management ◽  
Management Support ◽  
Cross Sectional Survey ◽  
Cross Sectional

Efficacy, effectiveness, and quality of resilience-building mobile health apps for military, veteran and public safety personnel populations: a scoping literature review and app evaluation (Preprint)

2021 ◽  
Author(s):  
Melissa Voth ◽  
Shannon Chisholm ◽  
Hannah Sollid ◽  
Chelsea Jones ◽  
Lorraine Smith-MacDonald ◽  
...  
Keyword(s):  
Mental Health ◽  
Literature Review ◽  
Mobile Health ◽  
Public Safety ◽  
Evidence Based ◽  
Inclusion Criteria ◽  
Public Safety Personnel ◽  
Scoping Literature Review ◽  
Mhealth Apps ◽  
Safety Personnel

BACKGROUND Globally, military members (MM) and public safety personnel (PSP) are vulnerable to occupational stress injuries (OSIs) due to their job demands. Consequently, when MM and PSP transition out of these professions, they may continue to experience mental health challenges. In response to this, resilience building programs are being developed and implemented with the goal of promoting empowerment and primary stress regulation. The development of mobile health (mHealth) applications (apps) as an emergent mental health intervention platform has allowed for targeted, cost effective, and easily accessible treatment when in-person therapy may be limited or unavailable. However, current mHealth app development is not regulated, and often lacks both clear evidence-based research and the input of healthcare professionals. OBJECTIVE The purpose of this manuscript is to evaluate the evidence-based quality, efficacy, and effectiveness of resilience building mobile apps targeted towards MM, PSP, and veteran populations via: (1) a scoping literature review of the current evidence-base regarding resilience apps for these populations, and; (2) evaluation of free resilience apps designed for use amongst these populations. METHODS Studies were selected using a comprehensive search of MEDLINE, CINAHL Plus, PsycINFO, SocINDEX, Academic Search Complete, Embase, and Google and was guided by Preferred Reporting Items for Systematic Reviews and Meta-Analysis for scoping reviews (PRISMA-ScR). The Alberta Rating Index for Apps (ARIA) was utilized to conduct a review of each of the identified apps. Inclusion criteria consisted of apps: 1) free to download in either Google Play or the App Store; 2) updated within the last 3 years; 3) available in English and in Canada; and 4) intended for use by MM and/or PSP. RESULTS Twenty-two apps met the inclusion criteria for evaluation. The resilience strategies offered by the majority of apps included psychoeducation, mindfulness, Cognitive Behavioural Therapy (CBT), and Acceptance and Commitment Therapy (ACT). Eleven apps (50%) had been tested with randomized controlled trials, seven (31.8%) were evaluated using other research methods, and five (22.7%) had not been researched. Using the ARIA, apps scores ranged from 37 to 56 out of 72 with higher rated apps demonstrating increased useability and security features. CONCLUSIONS The mHealth apps reviewed are well suited to providing resilience strategies for MMs, PSP and veterans. They offer easy accessibility to evidence-based tools while working to encourage the use of emotional and professional support with safety in mind. While not intended to function as a substitute for professional services, research has demonstrated that mHealth apps have the potential to foster a significant reduction in symptom severity for PTSD, depression, anxiety, and other stress-induced concerns. Within clinical practice, apps can be utilized to supplement treatment as well as provide clients with population-specific, confidential tools to increase engagement in the treatment process. CLINICALTRIAL N/A

scholarly journals Making Self-Management Mobile Health Apps Accessible to People With Disabilities: Qualitative Single-Subject Study

10.2196/15060 ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. e15060 ◽  
Author(s):  
Leming Zhou ◽  
Andi Saptono ◽  
I Made Agus Setiawan ◽  
Bambang Parmanto
Keyword(s):  
Mobile Health ◽  
Motor Impairment ◽  
People With Disabilities ◽  
Self Management ◽  
Fine Motor ◽  
Single Subject ◽  
Study Results ◽  
Cord Injury ◽  
Study Participants ◽  
Mhealth Apps

Background Over the past decade, a large number of mobile health (mHealth) apps have been created to help individuals to better manage their own health. However, very few of these mHealth apps were specifically designed for people with disabilities, and only a few of them have been assessed for accessibility for people with disabilities. As a result, people with disabilities have difficulties using many of these mHealth apps. Objective The objective of this study was to identify an approach that can be generally applied to improve the accessibility of mHealth apps. Methods We recruited 5 study participants with a primary diagnosis of cerebral palsy or spinal cord injury. All the participants had fine motor impairment or lack of dexterity, and hence, they had difficulties using some mHealth apps. These 5 study participants were first asked to use multiple modules in the client app of a novel mHealth system (iMHere 2.0), during which their performance was observed. Interviews were conducted post use to collect study participants’ desired accessibility features. These accessibility features were then implemented into the iMHere 2.0 client app as customizable options. The 5 participants were asked to use the same modules in the app again, and their performance was compared with that in the first round. A brief interview and a questionnaire were then performed at the end of the study to collect the 5 participants’ comments and impression of the iMHere 2.0 app in general and of the customizable accessibility features. Results Study results indicate that the study participants on their first use of the iMHere 2.0 client app experienced various levels of difficulty consistent with the severity of their lack of dexterity. Their performance was improved after their desired accessibility features were added into the app, and they liked the customizable accessibility features. These participants also expressed an interest in using this mHealth system for their health self-management tasks. Conclusions The accessibility features identified in this study improved the accessibility of the mHealth app for people with dexterity issues. Our approach for improving mHealth app accessibility may also be applied to other mHealth apps to make those apps accessible to people with disabilities.

scholarly journals Prevalence of anxiety and depressive symptoms and impact on self-management among adults with chronic conditions in Chicago, Illinois, USA, during the COVID-19 pandemic: a cross-sectional survey

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e052495
Author(s):  
Rebecca M Lovett ◽  
Lauren Opsasnick ◽  
Andrea Russell ◽  
Esther Yoon ◽  
Sophia Weiner-Light ◽  
...  
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Chronic Conditions ◽  
Least Square ◽  
Self Management ◽  
Secondary Outcome ◽  
Measurement Information ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Patient Reported

ObjectivesTo examine the prevalence of mental health symptoms during the first surge of COVID-19 in the USA, and their associations with COVID-19-related emotional distress, health self-management and healthcare utilisation.DesignCross-sectional analysis of wave 3 (1–22 May 2020) survey data from the ongoing Chicago COVID-19 Comorbidities (C3) study.SettingSeven academic and community health centres in Chicago, Illinois.Participants565 adults aged 23–88 with one or more chronic conditions completing at least one prior C3 study wave.Primary and secondary outcome measuresClinically relevant anxiety and depressive symptoms as measured using Patient-Reported Outcomes Measurement Information System short forms. Self-reported emotional and health-related responses to COVID-19 were measured through a combination of single-item questions and validated measures.ResultsRates of anxiety and depressive symptoms were 14% (81/563) and 15% (84/563), respectively. Anxiety and depressive symptoms were then each separately associated with greater worry about contracting COVID-19 (relative risk (RR) 2.32, 95% CI 1.52 to 3.53; RR 1.67, 95% CI 1.10 to 2.54), greater stress (RR 4.93, 95% CI 3.20 to 7.59; RR 3.01, 95% CI 1.96 to 4.61) and loneliness (RR 3.82, 95% CI 2.21 to 6.60; RR 5.37, 95% CI 3.21 to 8.98), greater avoidance of the doctor (RR 1.62, 95% CI 1.06 to 2.49; RR 1.54, 95% CI 1.00 to 2.36) and difficulty managing health (least square means (LS Means) 6.09, 95% CI 5.25 to 6.92 vs 4.23, 95% CI 3.70 to 4.75; LS Means 5.85, 95% CI 5.04 to 6.65 vs 4.22, 95% CI 3.70 to 4.75) and medications (LS Means 3.71, 95% CI 2.98 to 4.43 vs 2.47, 95% CI 2.02 to 2.92) due to the pandemic.ConclusionsIdentifying and addressing mental health concerns may be an important factor to consider in COVID-19 prevention and management among high-risk medical populations.

The Relationship between Resilience and Mental Health: the Mediating Effect of Positive Emotions

2018 ◽  
Vol 2 (3) ◽  
pp. 87
Author(s):  
Zhun Gong ◽  
Lichao Yu ◽  
Jonathan W Schooler
Keyword(s):  
Mental Health ◽  
Negative Correlation ◽  
Psychological Health ◽  
Positive Emotions ◽  
Mediating Effect ◽  
Depression And Anxiety ◽  
Cross Sectional ◽  
Positive Correlation ◽  
Relationship Of ◽  
The Relationship

<p class="tgt"><em>To investigate the relationship of resilience, positive emotions and mental health, and the relationship of resilience, positive emotion and three sub-dimensions of mental health: self-affirmation, depression and anxiety. In this study, the existing cross-sectional data, select the Beijing Forestry University data as samples. In this study, questionnaire survey a random sample of 199 undergraduate students of Beijing Forestry University, they uniform application three Scale Surveying, PANAS, CD-RISC, GHQ-20. According from the study, (1) resilience, positive mood and general health are related where resilience and positive emotions between the resilience. General psychological health, positive emotions and general mental health</em><em>?</em><em>it is positively correlated. (2) Resilience and self-affirmation exists, positive correlation with depression and anxiety, respectively negative correlation. Between positive emotions and self-affirmation the positive correlation with anxiety negative correlation. (3) Part mediating effect of positive emotions exist between resilience and self-affirmation, resilience can be made to self-affirmation prediction coefficient from 0.042 down to 0.036. Therefore, this study concluded that resilience undergraduates can have an impact on mental health through the intermediary variable positive emotions.</em></p>

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