JMIR Publications

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  mHealth for Data Collection and Research

  Oct 17, 2019

  The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study

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  Authors List:

  • Myeongchan Kim  
  • Sehyo Yune  
  • Seyun Chang  
  • Yuseob Jung  
  • Soon Ok Sa  
  • Hyun Wook Han  

  Abstract:

  Background: Effective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease. Objective: This study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities. Methods: Fever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child’s age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC). Results: During the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman ρ=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC. Conclusions: Fever Coach app successfully collected data from 7.73% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network.

  • JMIR Mhealth Uhealth 2019;7(10):e14276

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  mHealth for Treatment Adherence

  Oct 16, 2019

  Electronic Health Interventions to Improve Adherence to Antiretroviral Therapy in People Living With HIV: Systematic Review and Meta-Analysis

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  Authors List:

  • Ziqi Wang  
  • Yaxin Zhu  
  • Liyuan Cui  
  • Bo Qu  

  Abstract:

  Background: Electronic health (eHealth) is increasingly used for self-management and service delivery of HIV-related diseases. With the publication of increasingly studies focusing on antiretroviral therapy (ART) adherence, this makes it possible to quantitatively and systematically assess the effectiveness and feasibility of eHealth interventions. Objective: The purpose of this review was to explore the effectiveness of eHealth interventions on improving ART adherence in people living with HIV (PLWH). The effects of different intervention characteristics, participant characteristics, and study characteristics were also assessed. Methods: We systematically searched MEDLINE (via PubMed), EMBASE, the Cochrane Central Register of Controlled Trials, and 3 conference abstract databases using search terms related to HIV, ART, adherence, and eHealth interventions. We independently screened the studies, extracted the data, and assessed the study quality and then compared the information in pairs. Articles published in English that used randomized controlled trials to assess eHealth interventions to improve ART adherence of PLWH were identified. We extracted the data including study characteristics, participant characteristics, intervention characteristics, and outcome measures. The Cochrane risk-of-bias tool was used to assess the risk of bias and study overall quality. Odds ratios, Cohen d, and their 95% CIs were estimated using random-effects models. We also performed multiple subgroup analyses and sensitivity analyses to define any sources of heterogeneity. Results: Among 3941 articles identified, a total of 19 studies (including 21 trials) met the inclusion criteria. We found 8 trials from high-income countries and 13 trials from low- and middle-income countries. Furthermore, at baseline, the health status of participants in 14 trials was healthy. Of the trials included, 7 of 21 used personality content, 12 of 21 used a 2-way communication strategy, and 7 of 21 used medical content. In the pooled analysis of 3937 participants (mean age: 35 years; 47.16%, 1857/3937 females), eHealth interventions significantly improved the ART adherence of PLWH (pooled Cohen d=0.25; 95% CI 0.05 to 0.46; P=.01). The interventions were also correlated with improved biochemical outcomes reported by 11 trials (pooled Cohen d=0.25; 95% CI 0.11 to 0.38; P<.001). The effect was sensitive to sample size (Q=5.56; P=.02) and study duration (Q=8.89; P=.003), but it could not be explained by other moderators. The primary meta-analysis result was stable in the 3 sensitivity analyses. Conclusions: Some of the eHealth interventions may be used as an effective method to increase the ART adherence of PLWH. Considering that most of the trials included a small sample size and were conducted for a short duration, these results should be interpreted with caution. Future studies need to determine the features of eHealth interventions to better improve ART adherence along with long-term effectiveness of interventions, effectiveness of real-time adherence monitoring, enhancement of study design, and influences on biochemical outcomes.

  • JMIR Mhealth Uhealth 2019;7(10):e14404

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  Digital Biomarkers and Digital...

  Oct 16, 2019

  Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

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  Authors List:

  • Heejung Kim  
  • SungHee Lee  
  • SangEun Lee  
  • Soyun Hong  
  • HeeJae Kang  
  • Namhee Kim  

  Abstract:

  Background: Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups. Objective: The objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression. Methods: The prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted tree, and random forest models). Results: On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960). Conclusions: This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

  • JMIR Mhealth Uhealth 2019;7(10):e14149

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  mHealth in a Clinical Setting

  Oct 15, 2019

  Development of an Ambulatory Biofeedback App to Enhance Emotional Awareness in Patients with Borderline Personality Disorder: Multicycle Usability Testing Study

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  • Youri PMJ Derks  
  • Randy Klaassen  
  • Gerben J Westerhof  
  • Ernst T Bohlmeijer  
  • Matthijs L Noordzij  

  Abstract:

  Background: Patients with borderline personality disorder experience great difficulties in regulating their emotions. They often are unable to effectively detect their emotional arousal and struggle to timely apply learned techniques for emotion regulation. Although the use of continuous wearable biofeedback has been repeatedly suggested as an option to improve patients’ emotional awareness, this type of application is not yet available for clinical use. Therefore, we developed an ambulatory biofeedback app named Sense-IT that can be integrated in mental health care. Objective: The aim of the study was to develop an ambulatory biofeedback app for mental health care that helps with learning to better recognize changes in personal emotional arousal and increases emotional awareness. Methods: Using several methods in a tailored User Centred Design (UCD) framework, we tested the application’s usability and user experience (UX) via a cyclic developmental process with multiple user groups (patients, therapists and UCD experts; 3 – 5 per group, per cycle). Results: The process resulted in a stable prototype of the app that meets most of the identified user requirements. The app was valued as useful and usable by involved patients, therapists and UCD experts. On the Subjective Usability Scale (SUS), the patients rated the app as ‘Good’ (average score of 78.8), whereas the therapists rated the app as ‘OK’ (average score of 59.4). The UCD experts judged the app’s overall usability as between ‘OK’ and ‘acceptable’ (average score of 0.87 on a cognitive walkthrough). As most critical usability problems were identified and addressed in the first cycle of the prototyping process, subsequent cycles were mainly about implementing new or extending existing functions, and other adjustments to improve UX. Conclusions: mHealth development within a clinical mental health setting is challenging, yet feasible and welcomed by targeted users. This paper shows how new mHealth interventions for mental health care can be met with enthusiasm and openness by user groups that are known to be reluctant to embrace technological innovations. The use of the UCD framework, involving multiple user groups, proved to be of added value during design and realization as evidenced by the complementary requirements and perspectives. Future directions on studying clinical effectiveness of the app, appliance of the app in other fields, and the implications of integration of the app for daily practice in mental health are discussed.

  • JMIR Mhealth Uhealth 2019;7(10):e13479

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  mHealth for Wellness, Behavior Change...

  Oct 11, 2019

  Content and Feature Preferences for a Physical Activity App for Adults With Physical Disabilities: Focus Group Study

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  Authors List:

  • Sara H Olsen  
  • Sandra L Saperstein  
  • Robert S Gold  

  Abstract:

  Background: Hundreds of thousands of mobile phone apps intended to improve health and fitness are available for download across platforms and operating systems; however, few have been designed with people with physical disabilities in mind, ignoring a large population that may benefit from an effective tool to increase physical activity. Objective: This study represents the first phase in the development process of a fitness tracking app for people with physical disabilities interested in nontraditional sport. The aim of this research was to explore user preferences for content, appearance, and operational features of a proposed physical activity app for people with physical disabilities to inform the design of a mobile phone app for increasing physical activity. Methods: Four focus groups were conducted with 15 adults with physical disabilities who currently participate in nontraditional, non-Paralympic sport. Data collected from the focus group sessions centered on content, functionality, and appearance of apps currently used by participants as well as preferences for a future app. Results: Participants (mean age 35.7, SD 9.2 years) were mostly white (13/15, 87%), and all were currently participating in CrossFit and at least one other sport. Five main themes were identified. Themes included preferences for (1) workout-specific features that were tailored or searchable by disability, (2) user experience that was intuitive and accessible, (3) profile personalization options, (4) gamification features that allowed for competition with self and other users, and (5) social features that allowed increased interaction among users. Participants expressed a primary interest in having a fitness app that was designed for people with physical disabilities such that the features present in other fitness tracking apps were relevant to them and their community of adaptive athletes. Conclusions: The results showed that features related to user experience, social engagement, and gamification are considered important to people with physical disabilities. Features highlighted by participants as most desired, from a consumer perspective, were in line with research identifying attributes of quality apps that use behavior change techniques to influence positive physical activity behavior change. Such insights should inform the development of any fitness app designed to integrate users with disabilities as a primary user base.

  • JMIR Mhealth Uhealth 2019;7(10):e15019

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  mHealth for Wellness, Behavior Change...

  Oct 10, 2019

  Young People’s Attitudes and Motivations Toward Social Media and Mobile Apps for Weight Control: Mixed Methods Study

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  Authors List:

  • Charoula Konstantia Nikolaou  
  • Zoey Tay  
  • Jodie Leu  
  • Salome Antonette Rebello  
  • Lisa Te Morenga  
  • Rob M Van Dam  
  • Michael Ernest John Lean  

  Abstract:

  • JMIR Mhealth Uhealth 2019;7(10):e11205

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  mHealth for Symptom and Disease...

  Oct 9, 2019

  A Smart Mobile Health Tool Versus a Paper Action Plan to Support Self-Management of Chronic Obstructive Pulmonary Disease Exacerbations: Randomized...

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  Authors List:

  • Lonneke Boer  
  • Erik Bischoff  
  • Maarten van der Heijden  
  • Peter Lucas  
  • Reinier Akkermans  
  • Jan Vercoulen  
  • Yvonne Heijdra  
  • Willem Assendelft  
  • Tjard Schermer  

  Abstract:

  Background: Many patients with chronic obstructive pulmonary disease (COPD) suffer from exacerbations, a worsening of their respiratory symptoms that warrants medical treatment. Exacerbations are often poorly recognized or managed by patients, leading to increased disease burden and health care costs. Objective: This study aimed to examine the effects of a smart mobile health (mHealth) tool that supports COPD patients in the self-management of exacerbations by providing predictions of early exacerbation onset and timely treatment advice without the interference of health care professionals. Methods: In a multicenter, 2-arm randomized controlled trial with 12-months follow-up, patients with COPD used the smart mHealth tool (intervention group) or a paper action plan (control group) when they experienced worsening of respiratory symptoms. For our primary outcome exacerbation-free time, expressed as weeks without exacerbation, we used an automated telephone questionnaire system to measure weekly respiratory symptoms and treatment actions. Secondary outcomes were health status, self-efficacy, self-management behavior, health care utilization, and usability. For our analyses, we used negative binomial regression, multilevel logistic regression, and generalized estimating equation regression models. Results: Of the 87 patients with COPD recruited from primary and secondary care centers, 43 were randomized to the intervention group. We found no statistically significant differences between the intervention group and the control group in exacerbation-free weeks (mean 30.6, SD 13.3 vs mean 28.0, SD 14.8 weeks, respectively; rate ratio 1.21; 95% CI 0.77-1.91) or in health status, self-efficacy, self-management behavior, and health care utilization. Patients using the mHealth tool valued it as a more supportive tool than patients using the paper action plan. Patients considered the usability of the mHealth tool as good. Conclusions: This study did not show beneficial effects of a smart mHealth tool on exacerbation-free time, health status, self-efficacy, self-management behavior, and health care utilization in patients with COPD compared with the use of a paper action plan. Participants were positive about the supportive function and the usability of the mHealth tool. mHealth may be a valuable alternative for COPD patients who prefer a digital tool instead of a paper action plan. Clinical Trial: : ClinicalTrials.gov NCT02553096; https://clinicaltrials.gov/ct2/show/NCT02553096.

  • JMIR Mhealth Uhealth 2019;7(10):e14408

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  mHealth in the Developing...

  Oct 7, 2019

  Mobile Health for First Nations Populations: Systematic Review

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  Authors List:

  • Georgina R Hobson  
  • Liam J Caffery  
  • Maike Neuhaus  
  • Danette H Langbecker  

  Abstract:

  Background: The ubiquitous presence and functionality of mobile devices offer potential for mobile health (mHealth) to create equitable health opportunities. While mHealth is used among First Nations populations to respond to health challenges, the characteristics, uptake, and effectiveness of these interventions are unclear. Objective: This review aimed to identify the characteristics of mHealth interventions (eg, study locations, health topic, and modality) evaluated with First Nations populations and to summarize the outcomes reported for intervention use, user perspectives including cultural responsiveness, and clinical effectiveness. In addition, the review sought to identify the presence of First Nations expertise in the design and evaluation of mHealth interventions with First Nations populations. Methods: The methods of this systematic review were detailed in a registered protocol with the International Prospective Register of Systematic Reviews (PROSPERO, CRD42019123276). Systematic searches of peer-reviewed, scientific papers were conducted across 7 databases in October 2018. Eligible studies had a primary focus on mHealth interventions with experimental or quasi-experimental design to respond to a health challenge with First Nations people from Canada, Australia, New Zealand, and the United States. Two authors independently screened records for eligibility and assessed risk of bias using the Joanna Briggs Institute checklists. Data were synthesized narratively owing to the mix of study designs, interventions, and outcomes. The review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Results: Searches yielded 1053 unique records, after review and screening, 13 studies (5 randomized controlled trials and 8 quasi-experimental designs) were included in the final analysis. Studies were conducted in Australia (n=9), the United States (n=2), and New Zealand (n=2). The most common health challenge addressed was mental health and suicide (n=5). Intervention modalities included text messaging (n=5), apps (n=4), multimedia messaging (n=1), tablet software (n=1), or a combination of short messaging service (SMS) and apps (n=1). Results showed mixed engagement with the intervention (n=3); favorable user perspectives, including acceptability and cultural appropriateness (n=6); and mixed outcomes for clinical effectiveness (n=10). A diverse range of risks of bias were identified, most commonly lack of clarity about allocation and blinding protocols and group treatment for randomized controlled trials and lack of control group and single outcome measures for quasi-experimental designs. First Nations expertise informed all mHealth studies, through authorship (n=8), affiliation with First Nations bodies (n=3), participatory study design (n=5), First Nations reference groups (n=5), or a combination of these. Conclusions: mHealth modalities, including SMS and apps, appear favorable for delivery of health interventions with First Nations populations, particularly in the area of mental health and suicide prevention. Importantly, First Nations expertise was strongly embedded within the studies, augmenting favorable use and user engagement. However, evidence of efficacy is limited.

  • JMIR Mhealth Uhealth 2019;7(10):e14877

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  Text-messaging (SMS, WeChat...

  Oct 7, 2019

  Dropout and Abstinence Outcomes in a National Text Messaging Smoking Cessation Intervention for Pregnant Women, SmokefreeMOM: Observational Study

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  Authors List:

  • Kristyn Kamke  
  • Emily Grenen  
  • Cendrine Robinson  
  • Sherine El-Toukhy  

  Abstract:

  Background: Population-level text messaging smoking cessation interventions may reduce racial and ethnic differences in smoking among pregnant women. Objective: Our objective was to examine racial and ethnic differences in dropout, response, and abstinence rates among users of a US national, publicly available text messaging cessation intervention targeting pregnant women, SmokefreeMOM. Methods: Participants were online subscribers to SmokefreeMOM who set a prospective quit date within 9 months before their due date. We examined demographics, smoking frequency, number of cigarettes smoked per day, and prequit time (up to 14 days of preparation time before quit date) as correlates of response rate and abstinence at 8 time points: quit date, day 7, day 14, day 21, day 28, day 35, day 42 (intervention end), and day 72 (1-month follow-up). We conducted survival analysis of time from quit date to dropout by race and ethnicity. Results: The mean age of the analytic sample of 1288 users was 29.46 (SD 7.11) years. Of these, 65.81% (848/1288) were white, 16.04% (207/1288) were black, 8.86% (114/1288) were Latina, and 9.29% (120/1288) were multiracial, American Indian/Alaska Native, Native Hawaiian Pacific Islander, or other; 82.68% (1065/1288) had some college education or less. Point-prevalence abstinence was 14.51% (157/1082) on quit day, 3.51% (38/1082) at intervention end, and 1.99% (21/1053) at 1-month follow-up. Black users (hazard ratio 0.68, 95% CI 0.51-0.91) and those with a high school degree or less (hazard ratio 0.66, 95% CI 0.49-0.89) or some college education (hazard ratio 0.75, 95% CI 0.57-0.99) were less likely to drop out than whites or users with a bachelor’s degree or higher. Response and abstinence rates were similar across race, ethnicity, and education. Conclusions: Enrollment was low among racial and ethnic minority women but high among less-educated women. Abstinence at intervention end and 1-month follow-up was lower than that in controlled trials of text messaging cessation interventions for pregnant women (range 7%-20%). Increasing the reach, engagement, and effectiveness of SmokefreeMOM, especially among women with high rates of smoking during pregnancy, must be prioritized.

  • JMIR Mhealth Uhealth 2019;7(10):e14699

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  mHealth in the Developing...

  Oct 7, 2019

  Investigating the Use of Mobile Health Interventions in Vulnerable Populations for Cardiovascular Disease Management: Scoping Review

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  Authors List:

  • Sahr Wali  
  • Neesha Hussain- Shamsy  
  • Heather Ross  
  • Joseph Cafazzo  

  Abstract:

  Background: Cardiovascular disease (CVD) has grown to become one of the leading causes of mortality worldwide. The advancements of CVD-related treatments have led to a decline in CVD prevalence among individuals in high-income countries (HICs). However, these improvements do not reflect the state of individuals in low- and middle-income countries (LMICs) and vulnerable subgroup populations in HICs, such as the Indigenous. To help minimize the health disparities in these populations, technology-based interventions have been offered as a potential solution, but there is concern regarding if they will be effective, or even needed, as these tools have been designed for use in HICs. Objective: The objective of this study was to explore how mobile health (mHealth) interventions currently assist individuals in Indigenous communities and LMICs with CVD management. Methods: A scoping review guided by the methods outlined by Arksey and O’Malley was conducted. A comprehensive search was completed by 2 reviewers in 5 electronic databases using keywords related to mobile health, cardiovascular disease, self-care, Indigenous communities, and LMICs. Studies were screened over 2 rounds and critically reviewed using a descriptive-analytical narrative method. Descriptive data were categorized into thematic groups reflecting the major findings related to the study objective. Results: We identified a total of 11 original articles and 11 review papers that met the criteria for this scoping review. The majority of the studies included a telemonitoring- and text messaging (short message service, SMS)–related feature associated with the intervention. The use of SMS was the most common approach to effectively promote disease management among individuals in both LMICs and Indigenous communities. However, customizing for cultural considerations within the design of the intervention was highlighted as a pivotal component to encourage CVD management. Specifically, individuals emphasized that the inclusion of collaborative partnerships with community members would strengthen the effectiveness of the intervention by ensuring it was designed with the appropriate context. Conclusions: Technology-based interventions used within Indigenous communities and LMICs have shown their potential to assist individuals with managing their condition. Although the literature available regarding this topic is limited, this review outlines key components to promote the effective use of these tools in the context of these vulnerable populations.

  • JMIR Mhealth Uhealth 2019;7(10):e14275

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  mHealth for Wellness, Behavior Change...

  Oct 4, 2019

  Nurse-Driven mHealth Implementation Using the Technology Inpatient Program for Smokers (TIPS): Mixed Methods Study

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  Authors List:

  • Amanda C Blok  
  • Rajani S Sadasivam  
  • Timothy P Hogan  
  • Angela Patterson  
  • Nicole Day  
  • Thomas K Houston  

  Abstract:

  Background: Smoking is the leading cause of preventable death and disease, yet implementation of smoking cessation in inpatient settings is inconsistent. The Technology Inpatient Program for Smokers (TIPS) is an implementation program designed to reach smokers with an mHealth intervention using stakeholder-supported strategies. Objective: The purpose of this study was to determine the impact of the TIPS implementation strategies on smoker-level engagement of the mHealth intervention during care transition. Methods: We examined varying intensities (passive motivational posters only and posters + active nurse-led facilitation) of TIPS strategies on four hospital units located in two sites. Unit-level and smoker-level adoption was monitored during active implementation (30 weeks) and sustainability follow-up (30 weeks). Process measures reflecting the reach, effectiveness, adoption, implementation, maintenance (RE-AIM) framework, stakeholder reported adaptations of strategies, and formative evaluation data were collected and analyzed. Results: For our smoker-level reach, 103 smokers signed up for the mHealth intervention in-hospital, with minimal decline during sustainability follow-up. While posters + nurse facilitation did not lead to higher reach than posters alone during active implementation (27 vs 30 signed up), it did lead to higher engagement of smokers (85.2% vs 73.3% completion of the full 2-week intervention). TIPS strategy adoption and fidelity varied by unit, including adoption of motivational posters (range: weeks 1 and 5), fidelity of posters (0.4% to 16.2% of posters missing per unit weekly) and internal facilitation of nurse training sessions (average of 2 vs 7.5 by site). Variable maintenance costs of the program totaled $6.63 ($683.28/103) per smoker reached. Reported family-member facilitation of mHealth sign-up was an observation of unintended behavior. Conclusions: TIPS is a feasible and low-cost implementation program that successfully engages smokers in an mHealth intervention and sustains engagement after discharge. Further testing of nurse facilitation and expanding reach to patient family and friends as an implementation strategy is needed.

  • JMIR Mhealth Uhealth 2019;7(10):e14331

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  mHealth for Wellness, Behavior Change...

  Oct 4, 2019

  End User–Informed Mobile Health Intervention Development for Adolescent Cannabis Use Disorder: Qualitative Study

  View Authors View Abstract

  Authors List:

  • Kara Bagot  
  • Elizabeth Hodgdon  
  • Natasha Sidhu  
  • Kevin Patrick  
  • Mikaela Kelly  
  • Yang Lu  
  • Eraka Bath  

  Abstract:

  Background: The rates of cannabis use continue to increase among adolescents and the current interventions have modest effects and high rates of relapse following treatment. There is increasing evidence for the efficacy of mobile technology–based interventions for adults with substance use disorders, but there is limited study of this technology in adolescents who use cannabis. Objective: The goal of our study was to elucidate elements of an app-based adjunctive intervention for cannabis cessation that resonate with adolescents who use cannabis. Methods: Adolescents, aged between 14 and 17 years, who used cannabis were recruited from San Diego County high schools. Semistructured focus groups (6 total; N=37) were conducted to examine the ways in which participants used smartphones, including the use of any health behavior change apps, as well as to elicit opinions about elements that would promote engagement with an app-based intervention for adolescent cannabis cessation. An iterative coding structure was used with first cycle structural coding, followed by pattern coding. Results: Themes that emerged from the analysis included (1) youth valued rewards to incentivize the progressive reduction of cannabis use, which included both nontangible rewards that mimic those obtained on social media platforms and prosocial activity-related rewards, (2) having the ability to self-monitor progression, (3) peer social support, (4) privacy and confidentiality discrete logo and name and usernames within the app, and (5) individualizing frequency and content of notifications and reminders. Conclusions: Integrating content, language, interfaces, delivery systems, and rewards with which adolescents who use cannabis are familiar, engage with on a day-to-day basis, and identify as relevant, may increase treatment engagement and retention for adolescents in substance use treatment. We may increase treatment effectiveness by adapting and individualizing current evidence-based interventions, so that they target the needs of adolescents and are more easily incorporated into their everyday routines.

  • JMIR Mhealth Uhealth 2019;7(10):e13691

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