Text-messaging to promote smoking cessation among individuals with opioid use disorder: quantitative and qualitative evaluation.

INTRODUCTION: Individuals with opioid use disorder (OUD) who smoke cigarettes have high tobacco-related comorbidities, lack of access to tobacco treatment, lack of inclusion in smoking cessation trials, and remain understudied in the mobile health field. The purpose of this study was to understand patients' with OUD perceptions of 1) text message programs to promote smoking cessation, 2) content and features to include in such a program, and 3) how message content should be framed. METHODS: From December 2018 to February 2019, we recruited 20 hospitalized individuals with a concurrent diagnosis of OUD and tobacco dependence at Boston Medical Center (BMC), the largest safety-net hospital in New England. We surveyed participants' cell phone use, their interest in a text message program to promote smoking cessation, and their reactions to and ratings of a series of 26 prototype texts. We then conducted open-ended interviews to elicit content and suggestions on how text message interventions can improve motivation to increase smoking cessation among individuals with OUD. The interviews also included open-ended inquiries exploring message ratings and message content, inquiries about preferences for message duration, frequency, and personalization. RESULTS: Quantitative analysis of questionnaire data indicated that the majority of participants owned a cell phone (95%, 19/20). Most participants (60%, 12/20) reported that they would be interested or very interested in receiving text messages about smoking cessation. Text messages about the health benefits of quitting were rated the highest among various categories of text messages. Qualitative analysis showed that almost every participant felt that text messages would help motivate smoking cessation given the support it would provide. CONCLUSIONS: This study demonstrates that individuals with OUD who smoke cigarettes perceive that a text message program designed to promote smoking cessation would motivate and support smoking cessation efforts. Our findings demonstrate that such a program is feasible as participants own cell phones, frequently send and receive text messages, and have unlimited text message plans. Findings from this study provide valuable insight into content and features to include when developing text message programs to address barriers to smoking cessation in individuals who have OUD and smoke cigarettes.

Mobile Health Interventions and RCTs: Structured Taxonomy and Research Framework.

Mobile Health Interventions (MHIs) have addressed a range of healthcare challenges and have been evaluated using Randomized Controlled Trials (RCTs) to establish clinical effectiveness. Using PRISMA we conducted a systematic literature review of RCTs for MHIs and identified 70 studies which were analyzed and classified using Nickerson-Varshney-Muntermann (NVM) taxonomy. From the resultant iterations of the taxonomy, we extracted insights from the categorized studies. RCTs cover a wide range of health conditions including chronic diseases, general wellness, unhealthy practices, family planning, end-of-life, and post-transplant care. The MHIs that were utilized by the RCTs were varied as well, although most studies did not find significant differences between MHIs and usual care. The challenges for MHI-based RCTs include the use of technologies, delayed outcomes, patient recruitment, patient retention, and complex regulatory requirements. These variances can lead to a higher rate of Type I/Type II errors. Further considerations are the impact of infrastructure, contextual and cultural factors, and reductions in the technological relevancy of the intervention itself. Finally, due to the delayed effect of most outcomes, RCTs of insufficient duration are unable to measure significant, lasting improvements. Using the insights from seventy identified studies, we developed a classification of existing RCTs along with guidelines for MHI-based RCTs and a research framework for future RCTs. The framework offers opportunities for (a) personalization of MHIs, (b) use of richer technologies, and (c) emerging areas for RCTs.

A Smart Toy to Enhance the Decision-Making Process at Children's Psychomotor Delay Screenings: A Pilot Study.

BACKGROUND: EDUCERE ("Ubiquitous Detection Ecosystem to Care and Early Stimulation for Children with Developmental Disorders") is an ecosystem for ubiquitous detection, care, and early stimulation of children with developmental disorders. The objectives of this Spanish government-funded research and development project are to investigate, develop, and evaluate innovative solutions to detect changes in psychomotor development through the natural interaction of children with toys and everyday objects, and perform stimulation and early attention activities in real environments such as home and school. Thirty multidisciplinary professionals and three nursery schools worked in the EDUCERE project between 2014 and 2017 and they obtained satisfactory results. Related to EDUCERE, we found studies based on providing networks of connected smart objects and the interaction between toys and social networks. OBJECTIVE: This research includes the design, implementation, and validation of an EDUCERE smart toy aimed to automatically detect delays in psychomotor development. The results from initial tests led to enhancing the effectiveness of the original design and deployment. The smart toy, based on stackable cubes, has a data collector module and a smart system for detection of developmental delays, called the EDUCERE developmental delay screening system (DDSS). METHODS: The pilot study involved 65 toddlers aged between 23 and 37 months (mean=29.02, SD 3.81) who built a tower with five stackable cubes, designed by following the EDUCERE smart toy model. As toddlers made the tower, sensors in the cubes sent data to a collector module through a wireless connection. All trials were video-recorded for further analysis by child development experts. After watching the videos, experts scored the performance of the trials to compare and fine-tune the interpretation of the data automatically gathered by the toy-embedded sensors. RESULTS: Judges were highly reliable in an interrater agreement analysis (intraclass correlation 0.961, 95% CI 0.937-0.967), suggesting that the process was successful to separate different levels of performance. A factor analysis of collected data showed that three factors, trembling, speed, and accuracy, accounted for 76.79% of the total variance, but only two of them were predictors of performance in a regression analysis: accuracy (P=.001) and speed (P=.002). The other factor, trembling (P=.79), did not have a significant effect on this dependent variable. CONCLUSIONS: The EDUCERE DDSS is ready to use the regression equation obtained for the dependent variable "performance" as an algorithm for the automatic detection of psychomotor developmental delays. The results of the factor analysis are valuable to simplify the design of the smart toy by taking into account only the significant variables in the collector module. The fine-tuning of the toy process module will be carried out by following the specifications resulting from the analysis of the data to improve the efficiency and effectiveness of the product.

Health Advice from Internet Discussion Forums: How Bad Is Dangerous?

BACKGROUND: Concerns over online health information-seeking behavior point to the potential harm incorrect, incomplete, or biased information may cause. However, systematic reviews of health information have found few examples of documented harm that can be directly attributed to poor quality information found online. OBJECTIVE: The aim of this study was to improve our understanding of the quality and quality characteristics of information found in online discussion forum websites so that their likely value as a peer-to-peer health information-sharing platform could be assessed. METHODS: A total of 25 health discussion threads were selected across 3 websites (Reddit, Mumsnet, and Patient) covering 3 health conditions (human immunodeficiency virus [HIV], diabetes, and chickenpox). Assessors were asked to rate information found in the discussion threads according to 5 criteria: accuracy, completeness, how sensible the replies were, how they thought the questioner would act, and how useful they thought the questioner would find the replies. RESULTS: In all, 78 fully completed assessments were returned by 17 individuals (8 were qualified medical doctors, 9 were not). When the ratings awarded in the assessments were analyzed, 25 of the assessments placed the discussion threads in the highest possible score band rating them between 5 and 10 overall, 38 rated them between 11 and 15, 12 rated them between 16 and 20, and 3 placed the discussion thread they assessed in the lowest rating band (21-25). This suggests that health threads on Internet discussion forum websites are more likely than not (by a factor of 4:1) to contain information of high or reasonably high quality. Extremely poor information is rare; the lowest available assessment rating was awarded only 11 times out of a possible 353, whereas the highest was awarded 54 times. Only 3 of 78 fully completed assessments rated a discussion thread in the lowest possible overall band of 21 to 25, whereas 25 of 78 rated it in the highest of 5 to 10. Quality assessments differed depending on the health condition (chickenpox appeared 17 times in the 20 lowest-rated threads, HIV twice, and diabetes once). Although assessors tended to agree on which discussion threads contained good quality information, what constituted poor quality information appeared to be more subjective. CONCLUSIONS: Most of the information assessed in this study was considered by qualified medical doctors and nonmedically qualified respondents to be of reasonably good quality. Although a small amount of information was assessed as poor, not all respondents agreed that the original questioner would have been led to act inappropriately based on the information presented. This suggests that discussion forum websites may be a useful platform through which people can ask health-related questions and receive answers of acceptable quality.

Construction and Content Validation of Mobile Devices' Application Messages about Food and Nutrition for DM2 Older Adults.

Older adults face a decline in the quality of their diet, which affects their health. The prevalence of DM2 is increasing, as are the associated complications. Effective nutrition education and mobile health (mHealth) interventions offer a viable solution in the scenario of the widespread use of mobile devices. This study aimed to develop and validate messages for a mobile application aimed at older adult Brazilians with DM2 who receive care at the Brazilian Unified Health System (SUS). The educational messages on healthy eating for older adults with DM2 were created from 189 excerpts selected from Brazilian official documents. A total of 37 messages were created, categorized into 20 educational, 12 motivational, and 5 congratulatory, all up to 120 characters. Twenty-one experts validated the messages for clarity and relevance, and 11 messages had to be revised to meet the criteria. Subsequently, the 36 messages approved by the experts were tested on a sample of 57 older adults, guaranteeing clarity rates of over 80%. This study developed and validated 36 messages for a mobile health app aimed at older adults with type 2 diabetes mellitus in Brazil. Expert evaluation ensured clarity and relevance, confirmed by older adult participants who evaluated clarity. This research highlights the potential of mHealth to overcome barriers to accessing healthcare in the SUS, emphasizing personalized interventions for the effective management of older adults' health.

Deep breathing in your hands: designing and assessing a DTx mobile app.

Digital Therapeutics (DTx) are experiencing rapid advancements within mobile and mental healthcare sectors, with their ubiquity and enhanced accessibility setting them apart as uniquely effective solutions. In this evolving context, our research focuses on deep breathing, a vital technique in mental health management, aiming to optimize its application in DTx mobile platforms. Based on well-founded theories, we introduced a gamified and affordance-driven design, facilitating intuitive breath control. To enhance user engagement, we deployed the Mel Frequency Cepstral Coefficient (MFCC)-driven personalized machine learning method for accurate biofeedback visualization. To assess our design, we enlisted 70 participants, segregating them into a control and an intervention group. We evaluated Heart Rate Variability (HRV) metrics and collated user experience feedback. A key finding of our research is the stabilization of the Standard Deviation of the NN Interval (SDNN) within Heart Rate Variability (HRV), which is critical for stress reduction and overall health improvement. Our intervention group observed a pronounced stabilization in SDNN, indicating significant stress alleviation compared to the control group. This finding underscores the practical impact of our DTx solution in managing stress and promoting mental health. Furthermore, in the assessment of our intervention cohort, we observed a significant increase in perceived enjoyment, with a notable 22% higher score and 10.69% increase in positive attitudes toward the application compared to the control group. These metrics underscore our DTx solution's effectiveness in improving user engagement and fostering a positive disposition toward digital therapeutic efficacy. Although current technology poses challenges in seamlessly incorporating machine learning into mobile platforms, our model demonstrated superior effectiveness and user experience compared to existing solutions. We believe this result demonstrates the potential of our user-centric machine learning techniques, such as gamified and affordance-based approaches with MFCC, which could contribute significantly to the field of mobile mental healthcare.

Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions.

Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.

Characterizing Technology Use and Preferences for Health Communication in South Asian Immigrants With Prediabetes or Diabetes: Cross-Sectional Descriptive Study.

BACKGROUND: Type 2 diabetes disproportionately affects South Asian subgroups. Lifestyle prevention programs help prevent and manage diabetes; however, there is a need to tailor these programs for mobile health (mHealth). OBJECTIVE: This study examined technology access, current use, and preferences for health communication among South Asian immigrants diagnosed with or at risk for diabetes, overall and by sex. We examined factors associated with interest in receiving diabetes information by (1) text message, (2) online (videos, voice notes, online forums), and (3) none or skipped, adjusting for sociodemographic characteristics and technology access. METHODS: We used baseline data collected in 2019-2021 from two clinical trials among South Asian immigrants in New York City (NYC), with one trial focused on diabetes prevention and the other focused on diabetes management. Descriptive statistics were used to examine overall and sex-stratified impacts of sociodemographics on technology use. Overall logistic regression was used to examine the preference for diabetes information by text message, online (videos, voice notes, or forums), and no interest/skipped response. RESULTS: The overall sample (N=816) had a mean age of 51.8 years (SD 11.0), and was mostly female (462/816, 56.6%), married (756/816, 92.6%), with below high school education (476/816, 58.3%) and limited English proficiency (731/816, 89.6%). Most participants had a smartphone (611/816, 74.9%) and reported interest in receiving diabetes information via text message (609/816, 74.6%). Compared to male participants, female participants were significantly less likely to own smartphones (317/462, 68.6% vs 294/354, 83.1%) or use social media apps (Viber: 102/462, 22.1% vs 111/354, 31.4%; WhatsApp: 279/462, 60.4% vs 255/354, 72.0%; Facebook: Messenger 72/462, 15.6% vs 150/354, 42.4%). A preference for receiving diabetes information via text messaging was associated with male sex (adjusted odds ratio [AOR] 1.63, 95% CI 1.01-2.55; P=.04), current unemployment (AOR 1.62, 95% CI 1.03-2.53; P=.04), above high school education (AOR 2.17, 95% CI 1.41-3.32; P<.001), and owning a smart device (AOR 3.35, 95% CI 2.17-5.18; P<.001). A preference for videos, voice notes, or online forums was associated with male sex (AOR 2.38, 95% CI 1.59-3.57; P<.001) and ownership of a smart device (AOR 5.19, 95% CI 2.83-9.51; P<.001). No interest/skipping the question was associated with female sex (AOR 2.66, 95% CI 1.55-4.56; P<.001), high school education or below (AOR 2.02, 95% CI 1.22-3.36; P=.01), not being married (AOR 2.26, 95% CI 1.13-4.52; P=.02), current employment (AOR 1.96, 95% CI 1.18-3.29; P=.01), and not owning a smart device (AOR 2.06, 95% CI 2.06-5.44; P<.001). CONCLUSIONS: Technology access and social media usage were moderately high in primarily low-income South Asian immigrants in NYC with prediabetes or diabetes. Sex, education, marital status, and employment were associated with interest in mHealth interventions. Additional support to South Asian women may be required when designing and developing mHealth interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03333044; https://classic.clinicaltrials.gov/ct2/show/NCT03333044, ClinicalTrials.gov NCT03188094; https://classic.clinicaltrials.gov/ct2/show/NCT03188094. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-019-3711-y.

mHealth Apps Using Behavior Change Techniques to Self-report Data: Systematic Review.

BACKGROUND: The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management. OBJECTIVE: This review aimed to identify behavior change techniques (BCTs) commonly used in mHealth, assess their effectiveness based on the evidence reported in interventions and reviews to highlight the most appropriate techniques to design an optimal strategy to improve adherence to data reporting, and provide recommendations for future interventions and research. METHODS: We performed a systematic review of studies published between 2010 and 2021 in relevant scientific databases to identify and analyze mHealth interventions using BCTs that evaluated their effectiveness in terms of user adherence. Search terms included a mix of general (eg, data, information, and adherence), computer science (eg, mHealth and BCTs), and medicine (eg, personalized medicine) terms. RESULTS: This systematic review included 24 studies and revealed that the most frequently used BCTs in the studies were feedback and monitoring (n=20), goals and planning (n=14), associations (n=14), shaping knowledge (n=12), and personalization (n=7). However, we found mixed effectiveness of the techniques in mHealth outcomes, having more effective than ineffective outcomes in the evaluation of apps implementing techniques from the feedback and monitoring, goals and planning, associations, and personalization categories, but we could not infer causality with the results and suggest that there is still a need to improve the use of these and many common BCTs for better outcomes. CONCLUSIONS: Personalization, associations, and goals and planning techniques were the most used BCTs in effective trials regarding adherence to mHealth apps. However, they are not necessarily the most effective since there are studies that use these techniques and do not report significant results in the proposed objectives; there is a notable overlap of BCTs within implemented app components, suggesting a need to better understand best practices for applying (a combination of) such techniques and to obtain details on the specific BCTs used in mHealth interventions. Future research should focus on studies with longer follow-up periods to determine the effectiveness of mHealth interventions on behavior change to overcome the limited evidence in the current literature, which has mostly small-sized and single-arm experiments with a short follow-up period.

Measures of Engagement With mHealth Interventions in Patients With Heart Failure: Scoping Review.

BACKGROUND: Despite the potential of mobile health (mHealth) interventions to facilitate the early detection of signs of heart failure (HF) decompensation and provide personalized management of symptoms, the outcomes of such interventions in patients with HF have been inconsistent. As engagement with mHealth is required for interventions to be effective, poor patient engagement with mHealth interventions may be associated with mixed evidence. It is crucial to understand how engagement with mHealth interventions is measured in patients with HF, and the effects of engagement on HF outcomes. OBJECTIVE: In this review, we aimed to describe measures of patient engagement with mHealth interventions and the effects of engagement on HF outcomes. METHODS: We conducted a systematic literature search in 7 databases for relevant studies published in the English language from 2009 to September 2021 and reported the descriptive characteristics of the studies. We used content analysis to identify themes that described patient engagement with mHealth interventions in the qualitative studies included in the review. RESULTS: We synthesized 32 studies that operationalized engagement with mHealth interventions in 4771 patients with HF (3239/4771, 67.88%, male), ranging from a sample of 7 to 1571 (median 53.3) patients, followed for a median duration of 90 (IQR 45-180) days. Patient engagement with mHealth interventions was measured only quantitatively based on system usage data in 72% (23/32) of the studies, only qualitatively based on data from semistructured interviews and focus groups in 6% (2/32) of studies, and by a combination of both quantitative and qualitative data in 22% (7/32) of studies. System usage data were evaluated using 6 metrics of engagement: number of physiological parameters transmitted (19/30, 63% studies), number of HF questionnaires completed (2/30, 7% studies), number of log-ins (4/30, 13% studies), number of SMS text message responses (1/30, 3% studies), time spent (5/30, 17% studies), and the number of features accessed and screen viewed (4/30, 13% studies). There was a lack of consistency in how the system usage metrics were reported across studies. In total, 80% of the studies reported only descriptive characteristics of system usage data. The emotional, cognitive, and behavioral domains of patient engagement were identified through qualitative studies. Patient engagement levels ranged from 45% to 100% and decreased over time. The effects of engagement on HF knowledge, self-care, exercise adherence, and HF hospitalization were inconclusive. CONCLUSIONS: The measures of patient engagement with mHealth interventions in patients with HF are underreported and lack consistency. The application of inferential analytical methods to engagement data is extremely limited. There is a need for a working group on mHealth that may consolidate the previous operational definitions of patient engagement into an optimal and standardized measure.

Mobile Health Interventions: Examining Medication Adherence Outcomes Among Patients With Cancer.

BACKGROUND: Treatment for cancer is trending toward oral therapies, which patients can self-manage from home. Proper adherence to oral therapy is vital to safe and optimal care in this setting. Mobile health interventions (i.e., text message reminders, mobile applications, and automated calls) are an evolving strategy aimed at improving medication adherence for patients on long-term oral therapies. OBJECTIVES: This review aims to provide an overview of research outcomes for the use of mobile health interventions among patients with cancer. METHODS: A comprehensive review of CINAHL®, MEDLINE®, and PubMed® was completed. Eleven articles were eligible for inclusion in this review. FINDINGS: Mobile health interventions are an acceptable approach among patients with cancer and may improve adherence outcomes for those at highest risk for suboptimal adherence.

Significant reduction in depressive symptoms among patients with moderately-severe to severe depressive symptoms after participation in a therapist-supported, evidence-based mobile health program delivered via a smartphone app.

Depression is a debilitating disorder associated with poor health outcomes, including increased comorbidity and early mortality. Despite the advent of new digital health interventions, few have been tested among patients with more severe forms of depression. As such, in an intent-to-treat study we examined whether 218 patients with at least moderately severe depressive symptoms (PHQ-9 ≥ 15) experienced significant reductions in depressive symptoms after participation in a therapist-supported, evidence-based mobile health (mHealth) program, Meru Health Program (MHP). Patients with moderately severe and severe depressive symptoms at pre-program assessment experienced significant decreases in depressive symptoms at end-of treatment (mean [standard deviation] PHQ-9 reduction = 8.30 [5.03], Hedges' g = 1.64, 95% CI [1.44, 1.85]). Also, 34% of patients with at least moderately severe depressive symptoms at baseline and 29.9% of patients with severe depressive symptoms (PHQ-9 ≥ 20) at baseline responded to the intervention at end-of-treatment, defined as experiencing ≥50% reduction in PHQ-9 score and a post-program PHQ-9 score lower than 10. Limitations include use lack of a control group and no clinical diagnostic information. Future randomized trials are warranted to test the MHP as a scalable solution for patients with more severe depressive symptoms.

Data-Driven Implications for Translating Evidence-Based Psychotherapies into Technology-Delivered Interventions.

Mobile mental health interventions have the potential to reduce barriers and increase engagement in psychotherapy. However, most current tools fail to meet evidence-based principles. In this paper, we describe data-driven design implications for translating evidence-based interventions into mobile apps. To develop these design implications, we analyzed data from a month-long field study of an app designed to support dialectical behavioral therapy, a psychotherapy that aims to teach concrete coping skills to help people better manage their mental health. We investigated whether particular skills are more or less effective in reducing distress or emotional intensity. We also characterized how an individual's disorders, characteristics, and preferences may correlate with skill effectiveness, as well as how skill-level improvements correlate with study-wide changes in depressive symptoms. We then developed a model to predict skill effectiveness. Based on our findings, we present design implications that emphasize the importance of considering different environmental, emotional, and personal contexts. Finally, we discuss promising future opportunities for mobile apps to better support evidence-based psychotherapies, including using machine learning algorithms to develop personalized and context-aware skill recommendations.

Can an Emoji a Day Keep the Doctor Away? An Explorative Mixed-Methods Feasibility Study to Develop a Self-Help App for Youth With Mental Health Problems.

Today's smartphones allow for a wide range of "big data" measurement, for example, ecological momentary assessment (EMA), whereby behaviours are repeatedly assessed within a person's natural environment. With this type of data, we can better understand - and predict - risk for behavioral and health issues and opportunities for (self-monitoring) interventions. In this mixed-methods feasibility study, through convenience sampling we collected data from 32 participants (aged 16-24) over a period of three months. To gain more insight into the app experiences of youth with mental health problems, we interviewed a subsample of 10 adolescents who received psycthological treatment. The results from this feasibility study indicate that emojis) can be used to identify positive and negative feelings, and individual pattern analyses of emojis may be useful for clinical purposes. While adolescents receiving mental health care are positive about future applications, these findings also highlight some caveats, such as possible drawback of inaccurate representation and incorrect predictions of emotional states. Therefore, at this stage, the app should always be combined with professional counseling. Results from this small pilot study warrant replication with studies of substantially larger sample size.