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.

Mental health recovery and its correlates: Perspectives from 2 countries (Preprint)

2020 ◽  
Author(s):  
Huiting Xie
Keyword(s):  
Mental Health ◽  
United States ◽  
Self Efficacy ◽  
The United States ◽  
Mental Illnesses ◽  
Health Conditions ◽  
Mental Health Recovery ◽  
Mental Health Conditions ◽  
Serious Mental Illnesses ◽  
Promote Mental Health

BACKGROUND Many people are affected by mental health conditions, yet its prevalence in certain populations are not well documented. OBJECTIVE The aim of this study is to describe the attributes of people with mental health conditions in U.S and SG in terms of: perception of mental health recovery and its correlates such as strengths self-efficacy, resourcefulness and stigma experience. With the findings, not only could the knowledge base for mental health recovery in both countries be enhanced but interventions and policies relating to self-efficacy, resourcefulness and de-stigmatization for mental health recovery could be informed. METHODS A A cross-sectional, descriptive study with convenience sample of 200 community dwelling adults were selected, 100 pax from the United States (U.S) and 100 pax from Singapore (SG). Adults with serious mental illnesses without substance abuse impacting on their recovery were recruited. Participants completed self-administered questionaires measuring their mental health recovery, strengths self-efficacy, resourcefulness and stigma experience. RESULTS This study offered the unique opportunity to examine mental health recovery as well as its correlates such as strengths self-efficacy, resourcefulness and stigma experience from both the United States and Singapore. While the perception of mental health recovery and positive attributes like strengths self-efficacy and resourcefulness remained strong in participants with serious mental illnesses across both countries, people with serious mental illnesses in both countries still experienced negative perception like stigma. The findings would not only inform strategies to promote mental health recovery but also enhance the focus on correlates such as strengths self-efficacy and resourcefulness across both countries. CONCLUSIONS The findings would not only inform strategies to promote mental health recovery but also enhance the focus on correlates such as strengths self-efficacy and resourcefulness across both countries.

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.

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 Effects of a mobile health intervention on activities of stress self-management for workers: A randomized controlled trial

2021 ◽  
Author(s):  
Young Joo Lee
Keyword(s):  
Mental Health ◽  
Stress Management ◽  
Perceived Stress ◽  
Mobile Health ◽  
Work Engagement ◽  
Health Intervention ◽  
Analysis Of Covariance ◽  
Self Management ◽  
Control Group ◽  
Anxiety Depression

Abstract Background: Mobile health interventions are being widely tried because of their attractive advantages. However, there is not enough evidence for its effectiveness. This study aimed to evaluate the effects of mobile app-based stress management intervention (mSMI) on highly stressed workers. Methods: A sample of 82 white-collar workers with elevated symptoms of perceived stress (Perceived Stress Scale-10≥22) were randomly assigned to the intervention or control group. The mSMI consisted of three modules: self-management including a work diary, counseling based on cognitive behavioral therapy and interventions focused on music, meditation, relaxation and image healings. Self-report data were collected the baseline and post-intervention. Study outcomes were perceived stress, anxiety, depression, and work engagement. Data were assessed using analysis of covariance with covariates. Results: There was significantly reduced perceived stress from baseline to 6 weeks in mSMI (t=5.788, p<.001) and control group (t=3.184, p=.003). After adjusting for covariates, the between-group difference in the perceived stress was significantly different (F=4.051, p=.048); however, the effect size was small. There was no significant intervention effect on anxiety, depression, and work engagement. The process evaluation indicated that most participants (85.3%) were satisfied with the intervention and their mental health benefited. Conclusion: This study found that mobile health intervention facilitated stress management for highly stressed workers. Further studies should address job-related outcomes and mental health symptoms in workers by applying the latest information technology and addressing the limitations of mobile interventions. Trial Registration: Not applicable.

scholarly journals Effects of a mobile health intervention on activities of stress self-management for workers: A randomized controlled trial

2021 ◽  
Author(s):  
Young Joo Lee
Keyword(s):  
Mental Health ◽  
Stress Management ◽  
Perceived Stress ◽  
Mobile Health ◽  
Work Engagement ◽  
Health Intervention ◽  
Analysis Of Covariance ◽  
Self Management ◽  
Control Group ◽  
Anxiety Depression

Abstract Background: Mobile health interventions are being widely tried because of their attractive advantages. However, there is not enough evidence for its effectiveness. This study aimed to evaluate the effects of mobile app-based stress management intervention (mSMI) on highly stressed workers.Methods: A sample of 82 white-collar workers with elevated symptoms of perceived stress (Perceived Stress Scale-10≥22) were randomly assigned to the intervention or control group. The mSMI consisted of three modules: self-management including a work diary, counseling based on cognitive behavioral therapy and interventions focused on music, meditation, relaxation and image healings. Self-report data were collected the baseline and post-intervention. Study outcomes were perceived stress, anxiety, depression, and work engagement. Data were assessed using analysis of covariance with covariates.Results: There was significantly reduced perceived stress from baseline to 6 weeks in mSMI (t=5.788, p<.001) and control group (t=3.184, p=.003). After adjusting for covariates, the between-group difference in the perceived stress was significantly different (F=4.051, p=.048); however, the effect size was small. There was no significant intervention effect on anxiety, depression, and work engagement. The process evaluation indicated that most participants (85.3%) were satisfied with the intervention and their mental health benefited.Conclusion: This study found that mobile health intervention facilitated stress management for highly stressed workers. Further studies should address job-related outcomes and mental health symptoms in workers by applying the latest information technology and addressing the limitations of mobile interventions.Trial Registration: Not applicable.

scholarly journals Explanatory Model of Perceived Stress in the General Population: A Cross-Sectional Study in Peru During the COVID-19 Context

2021 ◽  
Vol 12 ◽  
Author(s):  
Alicia Boluarte-Carbajal ◽  
Alba Navarro-Flores ◽  
David Villarreal-Zegarra
Keyword(s):  
Mental Health ◽  
General Population ◽  
Negative Affect ◽  
Regression Model ◽  
Perceived Stress ◽  
Self Efficacy ◽  
Explanatory Model ◽  
Health Conditions ◽  
Mental Health Conditions ◽  
Structural Regression

BackgroundThe COVID-19 pandemic had negatively impact mental health worldwide. High prevalence of stress had been previously reported in populations during this context. Many theoretical frameworks had been proposed for explaining the stress process, we aim to proposed and explanatory model for the genesis of perceived stress in Peruvian general population.MethodWe conducted an online survey in Peruvian general population assessing sociodemographic variables and evaluating mental health conditions by using The Perceived Stress Scale (PSS-10), Positive Affect and Negative Affect Scale (PANAS), Generalized Anxiety Disorder scale (GAD-7), Patient Health Questionnaire (PHQ-9), and a numerical rating scale (NRS) for fear of COVID-19. Correlation analysis was conducted for the variables of interest. Two regression models were constructed to explore related factor to the dimensions of perceived stress. Finally, a structural regression model was performed with the independent variables.ResultsData of 210 individuals was analyzed. Ages ranged from 15 to 74 years and 39% were women. Additionally, 65.2% of the participants had at least one mental health conditions (depression, anxiety, or stress symptoms). Perceived self-efficacy and positive affect (PA) were correlated, as perceived helplessness with anxious symptoms and negative affect (NA). Regression analysis showed that sex, anxiety symptoms, and NA explained perceived helplessness while positive and NA explained self-efficacy. The structural regression model analysis identified that fear of COVID-19 (composed of fear of infecting others and fear of contagion), predicted mental health conditions (i.e., depressive or anxiety symptoms); also, mental health conditions were predicted by PA and NA. Perceived helplessness and Perceived self-efficacy were interrelated and represented the perceived stress variable.ConclusionWe proposed an explanatory model of perceived stress based on two correlated dimensions (self-efficacy and helplessness) in the Peruvian general population during the context of the COVID-19 pandemic, with two out of three individuals surveyed having at least one mental health condition.

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