Technology-based Cognitive Behavioral Therapy Interventions.

Technology-delivered cognitive behavioral therapy (CBT) has enabled more people to access effective, affordable mental health care. This study provides an overview of the most common types of technology-delivered CBT, including Internet-delivered, smartphone app, and telehealth CBT, as well as their evidence for the treatment of a range of mental health conditions. We discuss gaps in the existing evidence and future directions in the field for the use of technology CBT interventions.

Understanding Patient Perspectives on the Use of Gamification and Incentives in mHealth Apps to Improve Medication Adherence: Qualitative Study.

BACKGROUND: Medication nonadherence remains a significant health and economic burden in many high-income countries. Emerging smartphone interventions have started to use features such as gamification and financial incentives with varying degrees of effectiveness on medication adherence and health outcomes. A more consistent approach to applying these features, informed by patient perspectives, may result in more predictable and beneficial results from this type of intervention. OBJECTIVE: This qualitative study aims to identify patient perspectives on the use of gamification and financial incentives in mobile health (mHealth) apps for medication adherence in Australian patients taking medication for chronic conditions. METHODS: A total of 19 participants were included in iterative semistructured web-based focus groups conducted between May and December 2022. The facilitator used exploratory prompts relating to mHealth apps, gamification, and financial incentives, along with concepts raised from previous focus groups. Transcriptions were independently coded to develop a set of themes. RESULTS: Three themes were identified: purpose-driven design, trust-based standards, and personal choice. All participants acknowledged gamification and financial incentives as potentially effective features in mHealth apps for medication adherence. However, they also indicated that the effectiveness heavily depended on implementation and execution. Major concerns relating to gamification and financial incentives were perceived trivialization and potential for medication abuse, respectively. CONCLUSIONS: The study's findings provide a foundation for developers seeking to apply these novel features in an app intervention for a general cohort of patients. However, the study highlights the need for standards for mHealth apps for medication adherence, with particular attention to the use of gamification and financial incentives. Future research with patients and stakeholders across the mHealth app ecosystem should be explored to formalize and validate a set of standards or framework.

A Digital Health Intervention Platform (Active and Independent Management System) to Enhance the Rehabilitation Experience for Orthopedic Joint Replacement Patients: Usability Evaluation Study.

BACKGROUND: Optimal rehabilitation programs for orthopedic joint replacement patients ensure faster return to function, earlier discharge from hospital, and improved patient satisfaction. Digital health interventions show promise as a supporting tool for re-enablement. OBJECTIVE: The main goal of this mixed methods study was to examine the usability of the AIMS platform from the perspectives of both patients and clinicians. The aim of this study was to evaluate a re-enablement platform that we have developed that uses a holistic systems approach to address the de-enablement that occurs in hospitalized inpatients, with the older adult population most at risk. The Active and Independent Management System (AIMS) platform is anticipated to deliver improved patient participation in recovery and self-management through education and the ability to track rehabilitation progression in hospital and after patient discharge. METHODS: Two well-known instruments were used to measure usability: the System Usability Scale (SUS) with 10 items and, for finer granularity, the User Experience Questionnaire (UEQ) with 26 items. In all, 26 physiotherapists and health care professionals evaluated the AIMS clinical portal; and 44 patients in hospital for total knee replacement, total hip replacement, or dynamic hip screw implant evaluated the AIMS app. RESULTS: For the AIMS clinical portal, the mean SUS score obtained was 82.88 (SD 13.07, median 86.25), which would be considered good/excellent according to a validated adjective rating scale. For the UEQ, the means of the normalized scores (range -3 to +3) were as follows: attractiveness=2.683 (SD 0.100), perspicuity=2.775 (SD 0.150), efficiency=2.775 (SD 0.130), dependability=2.300 (SD 0.080), stimulation=1.950 (SD 0.120), and novelty=1.625 (SD 0.090). All dimensions were thus classed as excellent against the benchmarks, confirming the results from the SUS questionnaire. For the AIMS app, the mean SUS score obtained was 74.41 (SD 10.26), with a median of 77.50, which would be considered good according to the aforementioned adjective rating scale. For the UEQ, the means of the normalized scores were as follows: attractiveness=2.733 (SD 0.070), perspicuity=2.900 (SD 0.060), efficiency=2.800 (SD 0.090), dependability=2.425 (SD 0.060), stimulation=2.200 (SD 0.010), and novelty=1.450 (0.260). All dimensions were thus classed as excellent against the benchmarks (with the exception of novelty, which was classed as good), providing slightly better results than the SUS questionnaire. CONCLUSIONS: The study has shown that both the AIMS clinical portal and the AIMS app have good to excellent usability scores, and the platform provides a solid foundation for the next phase of research, which will involve evaluating the effectiveness of the platform in improving patient outcomes after total knee replacement, total hip replacement, or dynamic hip screw.

Adapting an artificial intelligence sexually transmitted diseases symptom checker tool for Mpox detection: the HeHealth experience.

Artificial Intelligence (AI) applications have shown promise in the management of pandemics. In response to the global Monkeypox (Mpox) outbreak, the HeHealth.ai team leveraged an existing tool to screen for sexually transmitted diseases (STD) to develop a digital screening test for symptomatic Mpox using AI. Before the global Mpox outbreak, the team developed a smartphone app (HeHealth) where app users can use a smartphone to photograph their own penises to screen for symptomatic STD. The AI model initially used 5000 cases and a modified convolutional neural network to output prediction scores across visually diagnosable penis pathologies including syphilis, herpes simplex virus, and human papillomavirus. A total of about 22,000 users had downloaded the HeHealth app, and ~21,000 images were analysed using HeHealth AI technology. We then used formative research, stakeholder engagement, rapid consolidation images, a validation study, and implementation of the tool. A total of 1000 Mpox-related images had been used to train the Mpox symptom checker tool. Based on an internal validation, our digital symptom checker tool showed specificity of 87% and sensitivity of 90% for symptomatic Mpox. Several hurdles identified included issues of data privacy and security for app users, initial lack of data to train the AI tool, and the potential generalisability of input data. We offer several suggestions to help others get started on similar projects in emergency situations, including engaging a wide range of stakeholders, having a multidisciplinary team, prioritising pragmatism, as well as the concept that 'big data' in fact is made up of 'small data'.

The Google Health Digital Well-Being Study: Protocol for a Digital Device Use and Well-Being Study.

BACKGROUND: The impact of digital device use on health and well-being is a pressing question. However, the scientific literature on this topic, to date, is marred by small and unrepresentative samples, poor measurement of core constructs, and a limited ability to address the psychological and behavioral mechanisms that may underlie the relationships between device use and well-being. Recent authoritative reviews have made urgent calls for future research projects to address these limitations. The critical role of research is to identify which patterns of use are associated with benefits versus risks and who is more vulnerable to harmful versus beneficial outcomes, so that we can pursue evidence-based product design, education, and regulation aimed at maximizing benefits and minimizing the risks of smartphones and other digital devices. OBJECTIVE: The objective of this study is to provide normative data on objective patterns of smartphone use. We aim to (1) identify how patterns of smartphone use impact well-being and identify groups of individuals who show similar patterns of covariation between smartphone use and well-being measures across time; (2) examine sociodemographic and personality or mental health predictors and which patterns of smartphone use and well-being are associated with pre-post changes in mental health and functioning; (3) discover which nondevice behavior patterns mediate the association between device use and well-being; (4) identify and explore recruitment strategies to increase and improve the representation of traditionally underrepresented populations; and (5) provide a real-world baseline of observed stress, mood, insomnia, physical activity, and sleep across a representative population. METHODS: This is a prospective, nonrandomized study to investigate the patterns and relationships among digital device use, sensor-based measures (including both behavioral and physiological signals), and self-reported measures of mental health and well-being. The study duration is 4 weeks per participant and includes passive sensing based on smartphone sensors, and optionally a wearable (Fitbit), for the complete study period. The smartphone device will provide activity, location, phone unlocks and app usage, and battery status information. RESULTS: At the time of submission, the study infrastructure and app have been designed and built, the institutional review board of the University of Oregon has approved the study protocol, and data collection is underway. Data from 4182 enrolled and consented participants have been collected as of March 27, 2023. We have made many efforts to sample a study population that matches the general population, and the demographic breakdown we have been able to achieve, to date, is not a perfect match. CONCLUSIONS: The impact of digital devices on mental health and well-being raises important questions. The Digital Well-Being Study is designed to help answer questions about the association between patterns of smartphone use and well-being. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49189.

Veteran Experiences With an mHealth App to Support Measurement-Based Mental Health Care: Results From a Mixed Methods Evaluation.

BACKGROUND: Mental health conditions are highly prevalent among US veterans. The Veterans Health Administration (VHA) is committed to enhancing mental health care through the integration of measurement-based care (MBC) practices, guided by its Collect-Share-Act model. Incorporating the use of remote mobile apps may further support the implementation of MBC for mental health care. OBJECTIVE: This study aims to evaluate veteran experiences with Mental Health Checkup (MHC), a VHA mobile app to support remote MBC for mental health. METHODS: Our mixed methods sequential explanatory evaluation encompassed mailed surveys with veterans who used MHC and follow-up semistructured interviews with a subset of survey respondents. We analyzed survey data using descriptive statistics. We then compared responses between veterans who indicated having used MHC for ≥3 versus <3 months using χ2 tests. We analyzed interview data using thematic analysis. RESULTS: We received 533 surveys (533/2631, for a 20% response rate) and completed 20 interviews. Findings from these data supported one another and highlighted 4 key themes. (1) The MHC app had positive impacts on care processes for veterans: a majority of MHC users overall, and a greater proportion who had used MHC for ≥3 months (versus <3 months), agreed or strongly agreed that using MHC helped them be more engaged in their health and health care (169/262, 65%), make decisions about their treatment (157/262, 60%), and set goals related to their health and health care (156/262, 60%). Similarly, interviewees described that visualizing progress through graphs of their assessment data over time motivated them to continue therapy and increased self-awareness. (2) A majority of respondents overall, and a greater proportion who had used MHC for ≥3 months (versus <3 months), agreed/strongly agreed that using MHC enhanced their communication (112/164, 68% versus 51/98, 52%; P=.009) and rapport (95/164, 58% versus 42/98, 43%; P=.02) with their VHA providers. Likewise, interviewees described how MHC helped focus therapy time and facilitated trust. (3) However, veterans also endorsed some challenges using MHC. Among respondents overall, these included difficulty understanding graphs of their assessment data (102/245, 42%), not receiving enough training on the app (73/259, 28%), and not being able to change responses to assessment questions (72/256, 28%). (4) Interviewees offered suggestions for improving the app (eg, facilitating ease of log-in, offering additional reminder features) and for increasing adoption (eg, marketing the app and its potential advantages for veterans receiving mental health care). CONCLUSIONS: Although experiences with the MHC app varied, veterans were positive overall about its use. Veterans described associations between the use of MHC and engagement in their own care, self-management, and interactions with their VHA mental health providers. Findings support the potential of MHC as a technology capable of supporting the VHA's Collect-Share-Act model of MBC.

Evaluating the Energy Efficiency of Popular US Smartphone Health Care Apps: Comparative Analysis Study Toward Sustainable Health and Nutrition Apps Practices.

BACKGROUND: The emergence of smartphones has sparked a transformation across multiple fields, with health care being one of the most notable due to the advent of mobile health (mHealth) apps. As mHealth apps have gained popularity, there is a need to understand their energy consumption patterns as an integral part of the evolving landscape of health care technologies. OBJECTIVE: This study aims to identify the key contributors to elevated energy consumption in mHealth apps and suggest methods for their optimization, addressing a significant void in our comprehension of the energy dynamics at play within mHealth apps. METHODS: Through quantitative comparative analysis of 10 prominent mHealth apps available on Android platforms within the United States, this study examined factors contributing to high energy consumption. The analysis included descriptive statistics, comparative analysis using ANOVA, and regression analysis to examine how certain factors impact energy use and consumption. RESULTS: Observed energy use variances in mHealth apps stemmed from user interactions, features, and underlying technology. Descriptive analysis revealed variability in app energy consumption (150-310 milliwatt-hours), highlighting the influence of user interaction and app complexity. ANOVA verified these findings, indicating the critical role of engagement and functionality. Regression modeling (energy consumption = ß0 + ß1 × notification frequency + ß2 × GPS use + ß3 × app complexity + ε), with statistically significant P values (notification frequency with a P value of .01, GPS use with a P value of .05, and app complexity with a P value of .03), further quantified these bases' effects on energy use. CONCLUSIONS: The observed differences in the energy consumption of dietary apps reaffirm the need for a multidisciplinary approach to bring together app developers, end users, and health care experts to foster improved energy conservation practice while achieving a balance between sustainable practice and user experience. More research is needed to better understand how to scale-up consumer engagement to achieve sustainable development goal 12 on responsible consumption and production.

Examining health apps and wearable use in improving physical and mental well-being across U.S., China, and Singapore.

Health apps and wearables are touted to improve physical health and mental well-being. However, it is unclear from existing research the extent to which these health technologies are efficacious in improving physical and mental well-being at a population level, particularly for the underserved groups from the perspective of health equity and social determinants. Also, it is unclear if the relationship between health apps and wearables use and physical and mental well-being differs across individualistic, collectivistic, and a mix of individual-collectivistic cultures. A large-scale online survey was conducted in the U.S. (individualist culture), China (collectivist culture), and Singapore (mix of individual-collectivist culture) using quota sampling after obtaining ethical approval from the Institutional Review Board (IRB-2021-262) of Nanyang Technological University (NTU), Singapore. There was a total of 1004 respondents from the U.S., 1072 from China, and 1017 from Singapore. Data were analyzed using multiple regression and negative binomial regression. The study found that income consistently had the strongest relationship with physical and mental well-being measures in all three countries, while the use of health apps and wearables only had a moderate association with psychological well-being only in the US. Health apps and wearables were associated with the number of times people spent exercising and some mental health outcomes in China and Singapore, but they were only positively associated with psychological well-being in the US. The study emphasizes the importance of considering the social determinants, social-cultural context of the population, and the facilitating conditions for the effective use of digital health technologies. The study suggests that the combined use of both health apps and wearables is most strongly associated with better physical and mental health, though this association is less pronounced when individuals use only apps or wearables.

Can platform use patterns be an indicator of HIV-related risk and sub-group heterogeneity among men who have sex with men in Singapore: a latent class analysis.

Introduction: Low-level HIV epidemic settings like Singapore face the challenge of reaching men at-risk who have less contact with programmes. We investigated patterns of meeting platform use by men seeking male sexual partners (MSM) as potential marker of risk to differentiate sub-groups for interventions. Methods: Latent Class Analysis (LCA) was applied to a survey sample of MSM recruited from bars/clubs, saunas and a smartphone application, using purposive sampling. The best-fit LCA model which identified homogeneous sub-groups with similar patterns of meeting platform was factored in multivariable regression to identify associations with risk behaviors on the pathway to HIV infection. Results: Overall 1,141 MSM were recruited from bars/clubs (n = 426), saunas (n = 531), and online (n = 184). Five patterns emerged, reflecting salient platform use characteristics: Sauna-centric (SC; n = 413), App-centric (AC; n = 276), Multiple-platforms (MP; n = 123), Platform-inactive (PI; n = 257), and "Do not hook up" (DNH; n = 72) classes. Men in the SC and MP classes had high probabilities of using saunas to meet partners; SC were older and less likely to have disclosed their sexual orientation. The MP class had high probabilities of connecting across all platforms in addition to saunas and more likely to have disclosed their sexual orientation, than the PI class. Men in the SC and MP classes had twice the odds of reporting multiple sex partners (aORSC = 2.1; 95%CI: 1.33.2; aORMP = 2.2; 95%CI: 1.14.6). Single/non-partnered MSM and those using alcohol/drugs during sex had 1.7 (95%CI: 1.22.5) and 3.2 (95%CI: 2.05.1) the odds respectively, of reporting multiple sex partners. The SC and MP classes had higher odds of engaging in group sex while MSM using alcohol/drugs during sex had twice the odds of reporting group sex. Alcohol/drugs and group sex were independently associated with condomless sex (as was lower education). Group sex, alcohol/drugs during sex, disclosure of sexual orientation or being Singaporean/permanent resident were associated with recent testing for HIV. Discussion: The five distinct risk profiles identified can help tailor differentiated HIV interventions-combined with field knowledge and other prevention-to expand HIV self-testing, Pre-Exposure Prophylaxis and other services (e.g., Mpox vaccination) to sub-groups at risk.

Effect of Adding Personalized Instant Messaging Apps to a Brief Smoking Cessation Model in Community Smokers in Hong Kong: Pragmatic Randomized Clinical Trial.

BACKGROUND: While text messaging has proven effective for smoking cessation (SC), engagement in the intervention remains suboptimal. OBJECTIVE: This study aims to evaluate whether using more interactive and adaptive instant messaging (IM) apps on smartphones, which enable personalization and chatting with SC advisors, can enhance SC outcomes beyond the provision of brief SC advice and active referral (AR) to SC services. METHODS: From December 2018 to November 2019, we proactively recruited 700 adult Chinese daily cigarette users in Hong Kong. Participants were randomized in a 1:1 ratio. At baseline, all participants received face-to-face brief advice on SC. Additionally, they were introduced to local SC services and assisted in selecting one. The intervention group received an additional 26 personalized regular messages and access to interactive chatting through IM apps for 3 months. The regular messages aimed to enhance self-efficacy, social support, and behavioral capacity for quitting, as well as to clarify outcome expectations related to cessation. We developed 3 sets of messages tailored to the planned quit date (within 30 days, 60 days, and undecided). Participants in the intervention group could initiate chatting with SC advisors on IM themselves or through prompts from regular messages or proactive inquiries from SC advisors. The control group received 26 SMS text messages focusing on general health. The primary outcomes were smoking abstinence validated by carbon monoxide levels of <4 parts per million at 6 and 12 months after the start of the intervention. RESULTS: Of the participants, 505/700 (72.1%) were male, and 450/648 (69.4%) were aged 40 or above. Planning to quit within 30 days was reported by 500/648 (77.2%) participants, with fewer intervention group members (124/332, 37.3%) reporting previous quit attempts compared with the control group (152/335, 45.4%; P=.04). At the 6- and 12-month follow-ups (with retention rates of 456/700, 65.1%, and 446/700, 63.7%, respectively), validated abstinence rates were comparable between the intervention (14/350, 4.0%, and 19/350, 5.4%) and control (11/350, 3.1% and 21/350, 6.0%) groups. Compared with the control group, the intervention group reported greater utilization of SC services at 12 months (RR 1.26, 95% CI 1.01-1.56). Within the intervention group, engaging in chat sessions with SC advisors predicted better validated abstinence at 6 months (RR 3.29, 95% CI 1.13-9.63) and any use of SC services (RR 1.66, 95% CI 1.14-2.43 at 6 months; RR 1.67, 95% CI 1.26-2.23 at 12 months). CONCLUSIONS: An IM-based intervention, providing support and assistance alongside brief SC advice and AR, did not yield further increases in quitting rates but did encourage the utilization of SC services. Future research could explore whether enhanced SC service utilization leads to improved long-term SC outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03800719; https://clinicaltrials.gov/ct2/show/NCT03800719.

Effectiveness of Gerontechnology Empowerment Program on Awareness and Use of Mobile Apps Among Older Adults for Instrumental Activities of Daily Living: Protocol for a Cluster Randomized Controlled Trial.

BACKGROUND: Instrumental activities of daily living (iADLs) are crucial for older adults to live independently. Health care and technological advancements will increase the older adult population and life expectancy globally. Difficulties with iADLs impact older adults' quality of life. Mobile apps can assist older adults, but many require help due to limited awareness. Lack of awareness is a barrier to app use. Existing literature mainly covers health care and app design, needing more focus on iADL apps for older adults. OBJECTIVE: The study objectives encompass 2 main aspects: first, to evaluate the awareness, use, and factors influencing the use of apps among older adults for iADLs; and second, to create and assess the effectiveness of a gerontechnology empowerment program (GEP) for older adults on the awareness and use of apps for iADLs. METHODS: This research uses a quantitative approach divided into 2 distinct phases. In phase 1, we conduct a descriptive survey to assess the level of awareness and use of mobile apps for iADLs and identify the factors that influence the use of such apps among older adults. To ensure clarity and comprehension among participants, we provide them with a subject information sheet in both Kannada and English. The data collected during this phase enable us to gain insights into awareness levels, use patterns, and factors that shape older adults' use of apps for iADLs. The results serve as the foundation for designing the GEP. In phase 2, a cluster randomization method will be used to select older adults aged 60 to 75 years in Udupi district, Karnataka, India, who are active smartphone users. These participants will be divided into 2 groups: the experimental and the control groups. The experimental group will join the GEP. The sample size for phase 1 is 554, and phase 2 is 50. To assess the effectiveness of this program, we will measure the outcomes before and after its implementation using the same assessment tools used in phase 1. RESULTS: This study is funded by the Indian Council of Medical Research (Adhoc/193/2022/SBHSR on November 18, 2022). Phase 1 data collection is expected to be completed by November 2023, and phase 2 is scheduled to commence in the upcoming months. Phase 1 and 2 findings will be analyzed and discussed in the main paper, which we intend to submit to a high-quality peer-reviewed journal for publication. The research protocol, informed consent forms, and associated documentation received approval from institutional ethics committees (214/2020). CONCLUSIONS: Upon the successful testing of the GEP, it can be recommended that welfare departments encourage older adults to use mobile apps for iADLs and establish training programs to provide support to older adults in using these apps. TRIAL REGISTRATION: Clinical Trials Registry - India CTRI/2020/09/027977; https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=NDUxMzM=&Enc=&userName=027977. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53587.

Action sequence learning, habits, and automaticity in obsessive-compulsive disorder.

This study investigates the goal/habit imbalance theory of compulsion in obsessive-compulsive disorder (OCD), which postulates enhanced habit formation, increased automaticity, and impaired goal/habit arbitration. It directly tests these hypotheses using newly developed behavioral tasks. First, OCD patients and healthy participants were trained daily for a month using a smartphone app to perform chunked action sequences. Despite similar procedural learning and attainment of habitual performance (measured by an objective automaticity criterion) by both groups, OCD patients self-reported higher subjective habitual tendencies via a recently developed questionnaire. Subsequently, in a re-evaluation task assessing choices between established automatic and novel goal-directed actions, both groups were sensitive to re-evaluation based on monetary feedback. However, OCD patients, especially those with higher compulsive symptoms and habitual tendencies, showed a clear preference for trained/habitual sequences when choices were based on physical effort, possibly due to their higher attributed intrinsic value. These patients also used the habit-training app more extensively and reported symptom relief post-study. The tendency to attribute higher intrinsic value to familiar actions may be a potential mechanism leading to compulsions and an important addition to the goal/habit imbalance hypothesis in OCD. We also highlight the potential of smartphone app training as a habit reversal therapeutic tool.

Feasibility and Acceptability of a Mobile App-Based TEAM-CBT (Testing Empathy Assessment Methods-Cognitive Behavioral Therapy) Intervention (Feeling Good) for Depression: Secondary Data Analysis.

BACKGROUND: The Feeling Good App is an automated stand-alone digital mobile mental health tool currently undergoing beta testing with the goal of providing evidence-informed self-help lessons and exercises to help individuals reduce depressive symptoms without guidance from a mental health provider. Users work through intensive basic training (IBT) and ongoing training models that provide education regarding cognitive behavioral therapy principles from a smartphone. OBJECTIVE: The key objective of this study was to perform a nonsponsored third-party academic assessment of an industry-generated data set; this data set focused on the safety, feasibility, and accessibility of a commercial automated digital mobile mental health app that was developed to reduce feelings associated with depression. METHODS: The Feeling Good App development team created a waitlist cohort crossover design and measured symptoms of depression and anxiety using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and an app-specific measure of negative feelings called the 7 Dimension Emotion Slider (7-DES). The waitlist cohort crossover design divided the participants into 2 groups, where 48.6% (141/290) of the participants were given immediate access to the apps, while 51.4% (149/290) were placed on a 2-week waitlist before being given access to the app. Data collected by the Feeling Good App development team were deidentified and provided to the authors of this paper for analysis through a nonsponsored university data use agreement. All quantitative data were analyzed using SPSS Statistics (version 28.0; IBM Corp). Descriptive statistics were calculated for demographic variables. Feasibility and acceptability were descriptively assessed. All participants included in the quantitative data were given access to the Feeling Good App; this study did not include a control group. RESULTS: In terms of safety, there was no statistically significant change in suicidality from preintervention to postintervention time points (t288=0.0; P>.99), and there was a statistically significant decrease in hopelessness from preintervention to postintervention time points (F289=30.16; P<.01). In terms of acceptability, 72.2% (166/230) of the users who started the initial 2-day IBT went on to complete it, while 34.8% (80/230) of the users who started IBT completed the entirety of the apps' 4-week protocol (150/230, 65.22% dropout rate over 4 weeks). CONCLUSIONS: This study is the first reported proof-of-concept evaluation of the Feeling Good App in terms of safety, feasibility, and statistical trends within the data set. It demonstrates a feasible and novel approach to industry and academic collaboration in the process of developing a digital mental health technology translated from an existing evidence-informed treatment. The results support the prototype app as safe for a select nonclinical population. The app had acceptable levels of engagement and dropouts throughout the intervention. Those who stay engaged showed reductions in symptom severity of depression warranting further investigation of the app's efficacy.

Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

BACKGROUND: A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one's self-regulation over momentary eating impulses. OBJECTIVE: This study aims to examine the feasibility and effectiveness of a novel artificial intelligence-assisted weight management app in improving eating behaviors in a Southeast Asian cohort. METHODS: A single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation. RESULTS: The mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m2, and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean -0.32, SD 1.16; P<.001), snacking habits (mean -0.22, SD 1.12; P<.002), self-regulation of eating behavior (mean 0.08, SD 0.49; P=.007), depression (mean -0.12, SD 0.74; P=.007), and physical activity (mean 1288.60, SD 3055.20 metabolic equivalent task-min/day; P<.001). Forty-one participants reported skipping at least 1 meal (ie, breakfast, lunch, or dinner), summing to 578 (67.1%) of the 862 meals skipped. Of the 230 participants, 80 (34.8%) provided textual feedback that indicated satisfactory user experience with eTRIP. Four themes emerged, namely, (1) becoming more mindful of self-monitoring, (2) personalized reminders with prompts and chatbot, (3) food logging with image recognition, and (4) engaging with a simple, easy, and appealing user interface. The attrition rate was 8.4% (21/251). CONCLUSIONS: eTRIP is a feasible and effective weight management program to be tested in a larger population for its effectiveness and sustainability as a personalized weight management program for people with overweight and obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803.

Prediction of anemia in real-time using a smartphone camera processing conjunctival images.

Anemia is defined as a low hemoglobin (Hb) concentration and is highly prevalent worldwide. We report on the performance of a smartphone application (app) that records images in RAW format of the palpebral conjunctivae and estimates Hb concentration by relying upon computation of the tissue surface high hue ratio. Images of bilateral conjunctivae were obtained prospectively from a convenience sample of 435 Emergency Department patients using a dedicated smartphone. A previous computer-based and validated derivation data set associating estimated conjunctival Hb (HBc) and the actual laboratory-determined Hb (HBl) was used in deriving Hb estimations using a self-contained mobile app. Accuracy of HBc was 75.4% (95% CI 71.3, 79.4%) for all categories of anemia, and Bland-Altman plot analysis showed a bias of 0.10 and limits of agreement (LOA) of (-4.73, 4.93 g/dL). Analysis of HBc estimation accuracy around different anemia thresholds showed that AUC was maximized at transfusion thresholds of 7 and 9 g/dL which showed AUC values of 0.92 and 0.90 respectively. We found that the app is sufficiently accurate for detecting severe anemia and shows promise as a population-sourced screening platform or as a non-invasive point-of-care anemia classifier.

Functional capacity and risk of injury in CrossFit practitioners measured through smartphone apps.

BACKGROUND: CrossFit is a high intensity functional training that tends to challenge physical limits. The objectives of this study were to assess functional capacity, prevalence and risk of injury in CrossFit practitioners. METHODS: This cross-sectional, observational and prospective study evaluate the rate of injuries that occurred in CrossFit practitioners in the last 12 months and their functional capacities. The sample was given for convenience, with a total of 22 participants. Functional capacities and risk of injury were measured by functional tests using PHAST and Clinometer applications. The prevalence of injuries was cataloged using the Nordic Musculoskeletal Questionnaire. RESULTS: 5% of the injuries occurred in the neck; 9% in shoulder, hip, thighs, ankles and feet; 14% in the lumbar spine and knees. The worst functional results were for the shoulder medial rotation ROM test, where 86-95% of the athletes were classified as "Bad"; the dorsiflexion ROM test also performed poorly in 68% of athletes. CONCLUSION: This study shows that the CrossFit practice suggests that the injury prevalence is relatively low, affecting mainly knees, lumbar spine, wrists and hands. However, the risk of injuries shown by the functional musculoskeletal assessment is higher, especially in the shoulder and ankle, and it is important for the practitioner to realize a specific functional assessment before starting training.

Mobile health technologies for workers' health and wellbeing: A systematic search of mHealth applications in Brazil.

BACKGROUND: Inadequate working conditions and sedentary work can exert a negative impact on workers' health and wellbeing, leading to musculoskeletal disorders and disability. Mobile health (mHealth) applications (apps) have high potential for the self-management of workers' health. OBJECTIVE: To identify mHealth apps aimed at promoting workers' health and wellbeing available in Brazilian online stores and assess these apps in terms of engagement, functionality, aesthetics and information quality. METHODS: A systematic search for apps was conducted in the Brazilian online App Store and Play Store in December 2022. Only smartphone apps in Brazilian Portuguese directed at workers' health were assessed. The appraisal of the quality of the applications was performed using the Mobile App Rating Scale (MARS). RESULTS: Among the 3449 mHealth apps found, ten were eligible for inclusion. The mean overall score was 3.15 ± 0.91 on a scale of 1-5. The lowest score was found for the "credibility" item. Exercises and breaks were the most frequent strategies. Most apps provided low-quality information from questionable sources and therefore received a mean score of 2.1 ± 1.5 on the MARS information subscale. CONCLUSION: Ten relevant mHealth apps were eligible for inclusion. The mHealth apps for the promotion of workers' health and wellbeing currently available in Brazil exhibited moderate quality and limited functionality.