Background: The menopause transition is a significant life milestone that often brings physical and mental challenges, deeply impacting quality of life and work performance. Among menopause-related conditions, pelvic floor dysfunctions (PFDs) affect ~40-50% of postmenopausal women, including urinary or fecal incontinence, genito-pelvic pain, and pelvic organ prolapse. While pelvic floor muscle training (PFMT) is the primary treatment, access and adherence barriers leave many untreated, advocating for new care delivery models. Objective: Assess the outcomes of a digital pelvic program, combining PFMT and education, in postmenopausal women with PFDs. Methods: This prospective, longitudinal study evaluated engagement, safety, and clinical outcomes of a remote digital pelvic program among postmenopausal women (N=3051) from diverse socioeconomic backgrounds, suffering from PFDs. Education and PFMT sessions were delivered through a mobile app, supported by an intravaginal sensor that provided real-time biofeedback. The intervention was asynchronously monitored and tailored by a physical therapist specialized in pelvic health. Clinical measures assessed pelvic floor symptoms and their impact on daily life (Pelvic Floor Impact Questionnaire–short form-7 - PFIQ-7, Urinary Impact Questionnaire–short form 7 - UIQ-7, Colorectal-Anal Impact Questionnaire–short form 7 - CRAIQ-7, Pelvic Organ Prolapse Impact Questionnaire–short form 7 - POPIQ-7), mental health, and work productivity and activity impairment. Structural equation modeling and minimal clinically important change (MCIC) response rates were used for analysis. Results: The digital pelvic program had a high completion rate of 77.6% (2367/3051), as well as high engagement and satisfaction level (8.6 out of 10). The safety of the intervention was supported by the low number of adverse events reported (0.69%, 21/3051). The overall impact of pelvic floor symptoms in participant's daily lives decreased significantly (19.55 points 95%CI -22.22;-16.88, P<.001, response rate of 59.5% 95%CI 54.9;63.9), regardless of condition. Notably, non-work-related activities and productivity impairment were reduced by around half at intervention end (-18.09 95%CI -19.99;-16.20, 48.3%; and -15.08 95%CI -17.52;-12.64, 54.6%, respectively, P<.001). Mental health also improved, with 76.1% (95%CI 60.7;84.9, unadjusted: 65.1% N=97/149) and 54.1% (95%CI 39.0;68.5, unadjusted: 45.2%, N=70/155) of participants with moderate to severe symptomatology achieving the MCIC for anxiety and depression, respectively. Recovery was generally not influenced by the higher baseline symptoms’ burden in individuals with younger age, high body mass index (BMI), social deprivation and residence in urban areas, except for pelvic health symptoms where lower BMI levels (P=.02) and higher social deprivation (P=.04) were associated with a steeper recovery. Conclusions: This study demonstrates the feasibility, safety, and positive clinical outcomes of a fully remote digital pelvic program to significantly improve PFD symptoms, mental health, and work productivity in postmenopausal women, while enhancing equitable access to personalized interventions that empower women to manage their condition and improve their quality of life. Clinical Trial: ClinicalTrials.gov NCT05513417

Background: Accessible mental health care, delivered via mobile apps or web-based services, may be essential for military members, public safety personnel (PSP), and veterans, as they report numerous barriers to seeking in-person care and are at an increased risk for a number of psychological disorders. Objective: We aimed to identify, describe, and evaluate apps, resource banks (RBs), and web-based programs (WBPs), referred to as digital mental health interventions (DMHIs), recommended for military members, PSP, and veterans. A multidimensional and multisystemic view of resilience and well-being were maintained throughout this environmental scan. Methods: Information was gathered from a comprehensive review of peer-reviewed literature, a Google search, and a targeted search of websites relevant to the study populations. DMHIs aimed at supporting resilience or well-being were included in the review, including those published in peer-reviewed articles, and those offered to these populations without research or literature backing their use. Results: In total, 69 DMHIs were identified in this study, including 42 apps, 19 RBs, and 8 WBPs, and were described based on 3 questions related to purpose, strategies, and evidence from the adapted Mobile App Rating Scale and the Mobile App Rating Scale. Each WBP and RB was then reviewed via the adapted Mobile App Rating Scale and each app via the Alberta Rating Index for Apps (ARIA). Overall, 24 (35%) of the DMHIs were recommended for military members, 20 (29%) for PSP, and 41 (59%) for veterans. The most common aim across apps, RBs, and WBPs was to increase happiness and well-being, and the most common strategies were advice, tips, and skills training. In total, 2 apps recommended for military members—PTSD Coach and Virtual Hope Box—received a high rating on the ARIA subscales and have also been trialed in pilot randomized control trial (RCT) and RCT evaluations, respectively, with positive initial results. Similarly, 2 apps recommended for PSP—PeerConnect and R2MR—have been trialed in non-RCT studies, with partially positive outcomes or little to no contradictory evidence and received a high rating on the ARIA. Finally, 2 apps recommended for veteran populations—PTSD Coach and VetChange—received high ratings on the ARIA and have been trialed via pilot-RCT and RCT studies, respectively, with positive outcomes. Conclusions: In conclusion, there is a need for efficacy and effectiveness trials for DMHIs for military members, PSP, and veterans to ensure that they are effectively meeting the population’s needs. While there appears to be many promising DMHIs, further research is needed before these interventions continue to be promoted as effective and widely distributed.

Background: Tools for measuring clinical disability status in people with multiple sclerosis (MS) are limited. Accelerometry objectively assesses physical activity and circadian rhythmicity profiles in the real-world environment and may potentially distinguish levels of disability in MS. Objective: To determine if accelerometry can detect differences in physical activity and circadian rhythmicity patterns between relapsing-remitting MS (RRMS) and progressive MS (PMS). Methods: This study represents an analysis of the baseline data from the prospective HEAL-MS study. Participants were divided into three groups based on the Expanded Disability Status Scale (EDSS) criteria for sustained disability progression: RRMS-Stable, RRMS-Suspected progression, and PMS. Baseline visits occurred between January 2021 and March 2023. Clinical outcome measures were collected by masked examiners. Participants wore the GT9X Link Actigraph on their non-dominant wrists for two weeks. Results: A total of 253 participants were included: 86 RRMS-Stable, 82 RRMS-Suspected progression, and 85 PMS. Compared to RRMS, PMS participants had lower total activity counts (β: -0.32 [95% CI: -0.61, -0.03]), lower time spent in moderate-to-vigorous physical activity (β: -0.01 [-0.02, -0.004]), higher active-to-sedentary transition probability (β: 5.68 [1.86, 9.50]), lower amplitude (β: -0.0004 [-0.0008, -0.0001]), lower MESOR (β: -0.0009 [-0.002, -0.0002]), higher intra-daily variability (β: 4.64 [1.45, 7.84]), and lower inter-daily stability (β: -4.43 [-8.77, -0.10]). No significant differences were detected between the two RRMS subtypes except for lower relative amplitude in those with suspected progression. Conclusions: Accelerometry detected differences in physical activity patterns between RRMS and PMS. Longitudinal follow-up is underway to assess the potential for accelerometry to detect disability progression.

Background: Mobile health (mHealth) apps provide innovative solutions for improving treatment adherence, facilitating lifestyle modifications, and optimizing blood pressure control in patients with hypertension. Despite their potential benefits, the adoption and recommendation of mHealth apps by physicians in Germany remain limited. This reluctance may be due to a lack of understanding of the factors influencing physicians’ willingness to incorporate these digital tools into routine clinical practice. Understanding these factors is crucial for fostering greater integration of mHealth apps in hypertension care. Objective: The aim of this study was to explore the relationship between physicians’ information needs and acceptance factors, and how these elements can support the effective integration of mHealth apps into daily medical routines. Methods: We conducted a qualitative study involving 24 semistructured telephone interviews with physicians, including 14 cardiologists and 10 general practitioners, who are involved in the treatment of hypertensive patients. Participants were selected through purposive sampling to ensure a diverse range of perspectives. Thematic analysis was conducted using MAXQDA software (Verbi GmbH) to identify key themes and subthemes related to the acceptance and use of mHealth apps. Results: The analysis revealed significant variability in physicians’ information needs regarding mHealth apps, particularly concerning their functionalities, clinical benefits, and potential impact on patient outcomes. These informational gaps play a critical role in determining whether physicians are willing to recommend mHealth apps to their patients. Key determinants influencing acceptance were identified, including the availability of robust knowledge about the apps, high-quality and reliable data, generational shifts within the medical profession, solid evidence supporting the effectiveness of the mHealth apps, and clearly defined areas of application and responsibilities within the physician-patient relationship. The study found that acceptance of mHealth apps could be significantly increased through targeted educational initiatives, enhanced data quality, and better integration of these tools into existing clinical workflows. Furthermore, younger physicians, more familiar with digital technologies, demonstrated greater openness to using mHealth apps, suggesting that generational changes may drive future increases in adoption. Conclusions: The successful integration of mHealth apps into hypertension management requires a multifaceted approach that addresses both the informational and practical concerns of physicians. By disseminating comprehensive knowledge about the variety, functionality, and proven efficacy of hypertension-related mHealth apps, health care providers can be better equipped to use these tools effectively. This approach necessitates the implementation of various knowledge transfer strategies, such as targeted training programs, peer learning opportunities, and active engagement with digital health technologies. As physicians become more informed and confident in the use of mHealth apps, their acceptance and recommendation of these tools are likely to increase, leading to more widespread adoption. Overcoming current barriers related to information deficits and data quality is essential for ensuring that mHealth apps are optimally used in routine hypertension care, ultimately improving patient outcomes and enhancing the overall quality of care. Trial Registration: German Clinical Trials Register DRKS00029761; https://drks.de/search/de/trial/DRKS00029761

Background: Numerous mobile apps have been developed for patients with cancer. However, there is still no comprehensive app for patients with breast cancer that integrates evidence-based medical information, psychological support, and schedule management through a multidisciplinary medical approach. Objective: We aimed to investigate whether a mobile app designed to assist in the self-management of patients with breast cancer is feasible and positively affects their self-efficacy and other psychological aspects. Methods: The Cancer Manager (CAMA) app was developed to assist in the self-management of patients with breast cancer and survivors of cancer according to cancer trajectory. Its functionalities include providing evidence-based digitalized information created by experts, managing patients’ medication and medical appointment schedules, and providing a delayed question and answer system for patients to query health care professionals. In this nonrandomized intervention trial, we analyzed data from 66 patients with breast cancer, divided into experimental (CAMA: n=34, 52%) and control (treatment as usual: n=32, 48%) groups. Group allocation was determined based on the patient’s willingness to use the app and access to compatible smartphones. Outcome measures included the Korean version of the Cancer Survivor Self-Efficacy Scale, the Korean version of the Mini-Mental Adjustment to Cancer (K-Mini-MAC) Scale, the World Health Organization Quality of Life Brief Version, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Menopause Emotional Symptoms Questionnaire (MESQ). A user satisfaction survey was also conducted. Results: Throughout the intervention period, the CAMA group (vs treatment as usual group) demonstrated significant improvements in the seeking help and support subscale of the Korean version of the Cancer Survivor Self-Efficacy Scale (F_1,64=5.09; P=.03), the psychological well-being subscale of the World Health Organization Quality of Life Brief Version (F_1,64=5.48; P=.02), the anxious preoccupation subscale (F_1,64=5.49; P=.02) and positive attitude subscale (F_1,64=5.44; P=.02) of the K-Mini-MAC Scale, PHQ-9 (F_1,64=4.83; P=.03), GAD-7 (F_1,64=5.48; P=.02), and MESQ (F_1,64=4.30; P=.04). Changes in the anxious preoccupation subscale of the K-Mini-MAC Scale scores were positively correlated with changes in the PHQ-9 (r=0.46; P=.007) and GAD-7 (r=0.41; P=.02) scores and negatively correlated with changes in the positive attitude subscale of the K-Mini-MAC Scale scores (r=–0.36; P=.04). Changes in the PHQ-9 scores were positively correlated with changes in the GAD-7 (r=0.66; P<.001) and MESQ (r=0.35; P=.04) scores. The user satisfaction survey offered insights into the CAMA app’s positive impact; trust-building outcomes; and opportunities for enhancement, such as the inclusion of communication tools and continued content enrichment. Conclusions: The mobile app for breast cancer self-management, CAMA, was deemed feasible and showed promise in improving the patients’ self-efficacy regarding seeking help and support, positive attitude toward cancer, and psychological well-being. In addition, its use might help reduce anxious preoccupation with cancer, depressive mood, anxiety, and menopausal emotional symptoms. Trial Registration: Clinical Research Information Service KCT0007917; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=23348

Background: Blockchain technology has capabilities that can transform how sensitive personal health data are safeguarded, shared, and accessed in digital health research. Women’s health data are considered especially sensitive, given the privacy and safety risks associated with their unauthorized disclosure. These risks may affect research participation. Using a privacy-by-design approach, we developed 2 app-based women’s health research study prototypes for user evaluation and assessed how blockchain may impact participation. Objective: This study aims to seek the perspectives of women to understand whether applications of blockchain technology in app-based digital research would affect their decision to participate and contribute sensitive personal health data. Methods: A convergent, mixed methods, experimental design was used to evaluate participant perceptions and attitudes toward using 2 app-based women’s health research study prototypes with blockchain features. Prototype A was based on the status quo ResearchKit framework and had extensive electronic informed consent, while prototype B minimized study onboarding requirements and had no informed consent; the mechanisms of how the contributed data flowed and were made pseudonymous were the same. User evaluations were carried out in February and March 2021 and consisted of a think-aloud protocol, a perception survey, and a semistructured interview. Findings were mapped to the technology acceptance model to guide interpretation. Results: We recruited 16 representative female participants from 175 respondents. User evaluations revealed that while participants considered prototype B easier to use on intuitive navigation (theme 1) of specified tasks and comprehension (theme 2) of research procedures, prototype A trended toward being perceived more favorably than prototype B across most perception survey constructs, with an overall lower level of privacy concern (mean [SD]: 2.22 [1.10] vs 2.95 [1.29]) and perceived privacy risk (2.92 [1.46] vs 3.64 [1.73]) and higher level of perceived privacy (5.21 [1.26] vs 4.79 [1.47]), trust (5.46 [1.19] vs 4.76 [1.27]), and usability (67.81 [21.77] vs 64.84 [23.69]). Prototype B was perceived more favorably than prototype A with perceived control (4.92 [1.32] vs 4.89 [1.29]) and perceived ownership (5.18 [0.59] vs 5.01 [0.96]). These constructs, except for perceived ownership, were significantly correlated with behavioral intention to use the app (P<.05). Participants perceived the usefulness of these prototypes in relation to the value of research study to women’s health field (theme 3), the value of research study to self (theme 4), and the value of blockchain features for participation (theme 5). Conclusions: This study provides nuanced insights into how blockchain applications in app-based research remain secondary in value to participants’ expectations of health research, and hence their intention to participate and contribute data. However, with impending data privacy and security concerns, it remains prudent to understand how to best integrate blockchain technology in digital health research infrastructure.

Background: Smoking cessation applications (Apps) are increasingly being used to assist smokers in quitting. In China, whether behavioral science has been incorporated into smoking cessation Apps remains unknown. Objective: The current study aims to describe the usage of behavior change techniques (BCTs) among smoking cessation Apps available in China and to evaluate the relationship between BCTs utilization and the quality of included smoking cessation Apps. Methods: We searched eligible smoking cessation Apps twice on Sep 12 and Oct 4, 2022. We coded them with BCTs and assessed their quality by the Mobile App Rating Scale (MARS) and rating score in the App Store. Correlation analysis and linear regression analysis were used to assess the association between the number of BCTs used and the quality of Apps. Results: 9 Apps were included in the final analyses. The average number of BCTs being used was 11.44 ± 2.57, ranging from 5 to 29. The most frequently used BCTs were providing feedback on current smoking behavior, prompting review of goals, prompting self-monitoring of one’s smoking behavior, and assessing current and past smoking behavior. The average score of MARS for the Apps was 3.88, ranging from 3.29 to 4.46, which was positively correlated with the number of BCTs used (r=0.79, p<0.05). Conclusions: The usage of behavior change techniques (BCTs) in smoking cessation Apps is generally low. Even the most popular App did not fully use behavior change techniques and was not rated with high quality. Smoking cessation Apps should increase their adoption of behavior change techniques and improve their quality to maximize the effect of helping smokers quit smoking.

Background: Consumer-grade wearables allow researchers to capture a representative picture of human behavior in the real world over extended periods. However, maintaining users’ engagement remains a challenge and can lead to a decrease in compliance (e.g., wear time in the context of wearable sensors) over time (e.g., “wearables’ abandonment”). Objective: In this work, we analyzed datasets from diverse populations (e.g., caregivers for various health issues, college students, and pediatric oncology patients) to quantify the impact that wear time requirements can have on study results. We found evidence that emphasizes the need to account for participants’ wear time in the analysis of consumer-grade wearables data. In Aim 1, we demonstrated the sensitivity of parameter estimates to different data processing methods with respect to wear time. In Aim 2, we demonstrate that not all research questions necessitate the same wear time requirements; some parameter estimates are not sensitive to wear time. Methods: We analyzed 3 Fitbit datasets comprising 6 different clinical and healthy population samples. For Aim 1, we analyzed the sensitivity of average daily step count and average daily heart rate at the population sample and individual levels to different methods of defining “valid” days using wear time. For Aim 2, we evaluated whether some research questions can be answered with data from lower compliance population samples. We explored: (1) the estimation of the average daily step count, and (2) the estimation of the average heart rate while walking. Results: Aim 1: We found that the changes in population sample average daily step count could reach 2,000 steps for different methods of analysis and were dependent on the wear time compliance of the sample. As expected, population samples with a low daily wear time (under ~15 hours of wear time per day) showed the most sensitivity to changes in methods of analysis. On the individual level, we observed that around 15% of individuals had a difference in step count higher than 1,000 steps for four of the six population samples analyzed when using different data processing methods. Those individual differences were higher than 3,000 steps for close to 5% of individuals across all population samples. Average daily heart rate appeared to be robust to changes in wear time. Aim 2: We found that for five population samples out of six, around 11% of individuals had enough data for the estimation of average heart rate while walking but not for the estimation of their average daily step count. Conclusions: We leveraged datasets from diverse populations to demonstrate the direct relationship between parameter estimates from consumer-grade wearable devices and participants’ wear time. Our findings highlighted the importance of a thorough analysis of wear time when processing data from consumer-grade wearables to ensure the relevance and reliability of the associated findings.

Background: Tobacco use remains the leading preventable cause of morbidity and mortality in the United States. Novel interventions are needed to improve smoking cessation rates. Mindfulness-based interventions (MBIs) for cessation address tobacco use by increasing awareness of the automatic nature of smoking and related behaviors (eg, reactivity to triggers for smoking) from a nonjudgmental stance. Delivering MBIs for smoking cessation via innovative technologies allows for flexibility in the timing of intervention delivery, which has the potential to improve the efficacy of cessation interventions. Research shows MBIs target key mechanisms in the smoking cessation process and can be used to minimize drivers of smoking lapse. Objective: This single-arm study investigated the impact of mindfulness-based strategies and motivational messages on proximal outcomes, collected via ecological momentary assessment (EMA), relevant to tobacco abstinence via a microrandomized trial. This approach allows for the evaluation of intervention content on proximal outcomes (eg, reduced negative affect) that are thought to impact positive distal outcomes (eg, smoking abstinence). Methods: All participants were motivated to quit smoking, and the intervention they received included nicotine replacement therapy, brief individual counseling, and a 2-week Just-in-Time Adaptive Intervention (JITAI) with wearable sensors. Throughout the JITAI period, a single strategy was randomly pushed (vs not) multiple times per day through the smartphone application. An EMA next assessed negative affect, positive affect, mindfulness, abstinence self-efficacy, motivation to quit, craving, and smoking motives. The primary analyses evaluated differences in EMA outcomes (proximal) for when a strategy was pushed versus not pushed. Additional analyses evaluated changes in similar outcomes collected from surveys at the baseline and end-of-treatment visits. Results: Participants (N=38) were 63% (24/38) female, 18% (7/38) Hispanic or Latino, and 29% (11/38) African American. They had an average age of 49 years and smoked an average of 15 (SD 7.9) cigarettes per day. Results indicated that receiving the JITAI significantly reduced proximal negative affect in the second (and final) week of the intervention. Self-reports provided at baseline and end of treatment showed significant decreases in perceived stress, automaticity of smoking and craving, and a significant increase in abstinence self-efficacy. Increases in abstinence self-efficacy significantly predicted abstinence. Conclusions: To our knowledge, this is the first study to test the proximal impact of a mindfulness-based JITAI on key variables associated with smoking cessation. Our primary finding was that negative affect was lower following the completion of a strategy (vs when no strategy was delivered) in the final week of the JITAI. Among a larger sample size, future research should extend the length of the intervention to further evaluate the impact of the JITAI, as well as include a comparison condition to further evaluate how each component of the intervention uniquely impacts outcomes. Trial Registration: ClinicalTrials.gov NCT03404596; https://clinicaltrials.gov/study/NCT03404596

Background: Nearly one third of adults in the United States will meet criteria for alcohol use disorder (AUD) in their lifetime, yet fewer than ten percent of individuals who meet for AUD criteria will receive treatment for it. Mobile Health (mHealth) applications have been suggested as a potential mechanism for closing this treatment gap, yet there is a wide variety of quality and integrity within these apps, leading to potential harms to users. Objective: The aim of this paper is to systematically record and qualitatively examine user reviews or mHealth applications to identify features in the existing apps that may impact usefulness and adoption of them. Methods: The researchers used Apple App and Google Play stores to identify mHealth applications that were focused on modifying alcohol use and treating common comorbidities. Apps that were free without in-app purchases and provided multiple features for users were included. User reviews from the apps were downloaded and coded using content analysis. Results: A total of 425 unique apps were found in our search. Of these, the majority of apps (n = 301) were excluded from the present analyses for not focusing on reducing alcohol-related concerns (e.g., many apps were for purchasing alcohol). Eight apps were identified and had user reviews downloaded. The apps examined in the present study were: VetChange, SMART, DrinkCoach, SayingWhen, AlcoStat, Celebrate Recovery, TryDry, and Construction Industry Helpline. A total of 370 reviews were downloaded and 1353 phrases were coded from those reviews into a total of 11 codes. The five most common themes identified were praise (498 counts coded; 36.831%), tools (150 counts coded; 11.062%), suggestions for improvement (118 counts coded; 8.756%), criticism (105 counts coded; 7.768%), and tracking (104 counts coded; 7.724%). Conclusions: The current findings suggest that alcohol mobile app users broadly found the apps helpful in reducing their drinking or meeting their drinking goals. Users were able to identify features that they liked or found helpful in the apps, as well as provide concrete feedback about features that they would like included or improved. Specifically, flexible and expansive tracking features and comprehensive whole health tools were cited as valuable and desired. App developers and those looking to expand access to and uptake of alcohol reduction apps may find these user reviews helpful in guiding their app development.