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Journal Description

JMIR Formative Research publishes peer-reviewed, openly accessible papers containing results from process evaluations, feasibility/pilot studies and other kinds of formative research and preliminary results. While the original focus was on the design of medical- and health-related research and technology innovations, JMIR Formative Research publishes studies from all areas of medical and health research.

Formative research is research that occurs before a program is designed and implemented, or while a program is being conducted. Formative research can help

  • define and understand populations in need of an intervention or public health program
  • create programs that are specific to the needs of those populations
  • ensure programs are acceptable and feasible to users before launching
  • improve the relationship between users and agencies/research groups
  • demonstrate the feasibility, use, satisfaction with, or problems with a program before large-scale summative evaluation (looking at health outcomes)

Many funding agencies will expect some sort of pilot/feasibility/process evaluation before funding a larger study such as a Randomized Controlled Trial (RCT).

Formative research should be an integral part of developing or adapting programs, and should be used while the program is ongoing to help refine and improve program activities. Thus, formative evaluation can and should also occur in the form of a process evaluation alongside a summative evaluation such as an RCT.

This journal fills an important gap in the academic journals landscape, as it publishes sound and peer-reviewed formative research that is critical for investigators to apply for further funding, but that is usually not published in outcomes-focused medical journals aiming for impact and generalizability.

Summative evaluations of programs and apps/software that have undergone a thorough formative evaluation before launch have a better chance to be published in high-impact flagship journals; thus, we encourage authors to submit - as a first step - their formative evaluations in JMIR Formative Research (and their evaluation protocols in JMIR Research Protocols). 

JMIR Formative Research has been accepted for indexing in PubMed and PubMed Central.


Recent Articles:

  • Source: Image created by the authors; Copyright: The Authors; URL:; License: Licensed by JMIR.

    Use of a Mobile App to Augment Psychotherapy in a Community Psychiatric Clinic: Feasibility and Fidelity Trial


    Background: Even though 1 in 5 Americans experience some form of mental illness each year, 80% have been shown to discontinue psychotherapy prematurely. The traditional psychotherapy service delivery model, consisting of isolated clinical sessions, lacks the ability to keep patients engaged outside clinical sessions. Newer digital mental health platforms can address the clinical need for a robust tool that tracks mental well-being and improves engagement in patients with depressive symptoms. Objective: The primary goals of this feasibility study were to (1) assess compliance among providers and their patients with a digital mental health platform protocol, and (2) examine the usability and fidelity of a mobile app through structured participant feedback. Methods: A sample of 30 participants was recruited for a 5-week study from a community-based mental health clinic in Baltimore, Maryland, USA. Inclusion criteria were: aged 18 years or older, having access to a smartphone, and having at least mild-to-moderate depression and/or anxiety as measured by the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) scales, respectively. Eligible participants were randomized into one of two study arms: (1) the intervention arm or (2) the waitlist control arm. Participants in the intervention arm were asked to download the Rose app and were prompted to complete clinical assessments (PHQ-9 and GAD-7) every other week, daily mood and anxiety Likert scales, and daily journal entries. The participants in the waitlist arm served as controls for the study and completed the clinical assessments only. Both arms engaged in weekly psychotherapy sessions, with participant in-app input informing the psychotherapy process of the intervention arm, while those in the waitlist control arm continued their standard care. Outcomes of interest included adherence to completion of in-app assessments and usability of the Rose mobile app assessed through the modified Mobile Application Rating Scale. Results: Over the study period, a sample of 30 participants used the Rose app 2834 times to complete clinical assessments. On average, 70% (21; 95% CI 61.14%-77.41%) of participants completed mood and anxiety daily check-ins and journal entries 5 days per week. Nearly all participants (29/30, 97%) completed all PHQ-9 and GAD-7 in-app scales during the study. Subjective impressions showed that 73% (22/30) of participants found the mobile app to be engaging and in line with their needs, and approximately 70% (21/30) of participants reported the app functionality and quality of information to be excellent. Additionally, more than two-thirds of the participants felt that their knowledge and awareness of depression and anxiety management improved through using the app. Conclusions: Steady compliance and high app ratings showcase the utility of the Rose mobile mental health app in augmenting the psychotherapy process for patients with mood disorders and improving mental health knowledge. Future studies are needed to further examine the impact of Rose on treatment outcomes. Trial Registration: NCT04200170;

  • Source: Flickr; Copyright: NIAID; URL:; License: Creative Commons Attribution (CC-BY).

    Patient and Parent Perspectives on Improving Pediatric Asthma Self-Management Through a Mobile Health Intervention: Pilot Study


    Background: Asthma is a common chronic pediatric disease that can negatively impact children and families. Self-management strategies are challenging to adopt but critical for achieving positive outcomes. Mobile health technology may facilitate self-management of pediatric asthma, especially as adolescents mature and assume responsibility for their disease. Objective: This study aimed to explore the perceptions of youths with high-risk asthma and their caregivers on the use of a smartphone app, Smartphone Asthma Management System, in the prevention and treatment of asthma symptoms, possible use of the app to improve self-management of asthma outside traditional clinical settings, and the impact of asthma on everyday life to identify potential needs for future intervention development. Methods: Key informant interviews were completed with parent-child dyads post participation in an asthma management feasibility intervention study to explore the perceptions of users on a smartphone app designed to monitor symptoms and medication use and offer synchronous and asynchronous provider encounters. A thematic qualitative analysis was conducted inductively through emergent findings and deductively based on the self-determination theory (SDT), identifying 4 major themes. Results: A total of 19 parent-child dyads completed the postintervention interviews. The major themes identified included autonomy, competence, relatedness, and the impact of asthma on life. The participants also shared their perceptions of the benefits and challenges associated with using the app and in the self-management of asthma. Both children and parents conveyed a preference for using technology to facilitate medication and disease management, and children demonstrated a strong willingness and ability to actively engage in their care. Conclusions: Our study included support for the app and demonstrated the feasibility of enhancing the self-management of asthma by youth in the community. Participant feedback led to intervention refinement and app improvements, and the use of the SDT allowed insight into motivational drivers of behavioral change. The use of mobile apps among high-risk children with asthma and their parents shows promise in improving self-management, medication adherence, and disease awareness and in reducing overall disease morbidity.

  • Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Barriers to Gestational Diabetes Management and Preferred Interventions for Women With Gestational Diabetes in Singapore: Mixed Methods Study


    Background: Gestational diabetes mellitus (GDM) is associated with risks for both the mother and child. The escalated prevalence of GDM because of obesity and changes in screening criteria demands for greater health care needs than before. Objective: This study aimed to understand the perception of patients and health care providers of the barriers to GDM management and preferred interventions to manage GDM in an Asian setting. Methods: This mixed methods study used a convergent parallel design. Survey data were collected from 216 women with GDM, and semistructured interviews were conducted with 15 women and with 8 health care providers treating patients with GDM. Participants were recruited from 2 specialized GDM clinics at the National University Hospital, Singapore. Results: The patients were predominantly Chinese (102/214, 47.6%), employed (201/272, 73.9%), with higher education (150/216, 69.4%) and prenatal attendance at a private clinic (138/214, 64.2%), already on diet control (210/214, 98.1%), and receiving support and information from the GDM clinic (194/215, 90.2%) and web-based sources (131/215, 60.9%). In particular, working women reported barriers to GDM management, including the lack of reminders for blood glucose monitoring, diet control, and insufficient time for exercise. Most women preferred getting such support directly from health care providers, whether at the GDM clinic (174/215, 80.9%) or elsewhere (116/215, 53.9%). Smartphone apps were the preferred means of additional intervention. Desirable intervention features identified by patients included more information on GDM, diet and exercise options, reminders for blood glucose testing, a platform to record blood glucose readings and illustrate or understand trends, and a means to communicate with care providers. Conclusions: A GDM-focused smartphone app that is able to integrate testing, education, and communication may be a feasible and acceptable intervention to provide support to women with GDM, particularly for working women.

  • Source: Pexels; Copyright: Creative Vix; URL:; License: Licensed by JMIR.

    Smartphone Self-Monitoring by Young Adolescents and Parents to Assess and Improve Family Functioning: Qualitative Feasibility Study


    Background: The natural integration of mobile phones into the daily routines of families provides novel opportunities to study and support family functioning and the quality of interactions between family members in real time. Objective: This study aimed to examine user experiences of feasibility, acceptability, and reactivity (ie, changes in awareness and behaviors) of using a smartphone app for self-monitoring of family functioning with 36 participants across 15 family dyads and triads of young adolescents aged 10 to 14 years and their parents. Methods: Participants were recruited from 2 family wellness centers in a middle-to-upper income shopping area and a low-income school site. Participants were instructed and prompted by alarms to complete ecological momentary assessments (EMAs) by using a smartphone app over 2 weeks 4 times daily (upon waking in the morning, afternoon, early evening, and end of day at bedtime). The domains assessed included parental monitoring and positive parenting, parent involvement and discipline, parent-child conflict and resolution, positive interactions and support, positive and negative affect, sleep, stress, family meals, and general child and family functioning. Qualitative interviews assessed user experiences generally and with prompts for positive and negative feedback. Results: The participants were primarily white and Latino of mixed-income- and education levels. Children were aged 10 to 14 years, and parents had a mean age of 45 years (range 37-50). EMA response rates were high (95% to over 100%), likely because of cash incentives for EMA completion, engaging content per user feedback, and motivated sample from recruitment sites focused on social-emotional programs for family wellness. Some participants responded for up to 19 days, consistent with some user experience interview feedback of desires to continue participation for up to 3 or 4 weeks. Over 80% (25/31) of participants reported increased awareness of their families’ daily routines and functioning of their families. Most also reported positive behavior changes in the following domains: decision making, parental monitoring, quantity and quality of time together, communication, self-regulation of stress and conflict, discipline, and sleep. Conclusions: The results of this study support the feasibility and acceptability of using smartphone EMA by young adolescents and parents for assessing and self-monitoring family daily routines and interactions. The findings also suggest that smartphone self-monitoring may be a useful tool to support improvement in family functioning through functions of reflection on antecedents and consequences of situations, prompting positive and negative alternatives, seeding goals, and reinforcement by self-tracking for self-correction and self-rewards. Future studies should include larger samples with more diverse and higher-risk populations, longer study durations, the inclusion of passive phone sensors and peripheral biometric devices, and integration with counseling and parenting interventions and programs.

  • Source: Adobe Stock; Copyright: Maridav; URL:; License: Licensed by JMIR.

    Adherence of the #Here4U App – Military Version to Criteria for the Development of Rigorous Mental Health Apps


    Background: Over the past several years, the emergence of mobile mental health apps has increased as a potential solution for populations who may face logistical and social barriers to traditional service delivery, including individuals connected to the military. Objective: The goal of the #Here4U App – Military Version is to provide evidence-informed mental health support to members of Canada’s military community, leveraging artificial intelligence in the form of IBM Canada’s Watson Assistant to carry on unique text-based conversations with users, identify presenting mental health concerns, and refer users to self-help resources or recommend professional health care where appropriate. Methods: As the availability and use of mental health apps has increased, so too has the list of recommendations and guidelines for efficacious development. We describe the development and testing conducted between 2018 and 2020 and assess the quality of the #Here4U App against 16 criteria for rigorous mental health app development, as identified by Bakker and colleagues in 2016. Results: The #Here4U App – Military Version met the majority of Bakker and colleagues’ criteria, with those unmet considered not applicable to this particular product or out of scope for research conducted to date. Notably, a formal evaluation of the efficacy of the app is a major priority moving forward. Conclusions: The #Here4U App – Military Version is a promising new mental health e-solution for members of the Canadian Armed Forces community, filling many of the gaps left by traditional service delivery.

  • A user examines a core affect grid measurement on a smartphone running the Wear-IT application. Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Low-Burden Mobile Monitoring, Intervention, and Real-Time Analysis Using the Wear-IT Framework: Example and Usability Study


    Background: Mobile health (mHealth) methods often rely on active input from participants, for example, in the form of self-report questionnaires delivered via web or smartphone, to measure health and behavioral indicators and deliver interventions in everyday life settings. For short-term studies or interventions, these techniques are deployed intensively, causing nontrivial participant burden. For cases where the goal is long-term maintenance, limited infrastructure exists to balance information needs with participant constraints. Yet, the increasing precision of passive sensors such as wearable physiology monitors, smartphone-based location history, and internet-of-things devices, in combination with statistical feature selection and adaptive interventions, have begun to make such things possible. Objective: In this paper, we introduced Wear-IT, a smartphone app and cloud framework intended to begin addressing current limitations by allowing researchers to leverage commodity electronics and real-time decision making to optimize the amount of useful data collected while minimizing participant burden. Methods: The Wear-IT framework uses real-time decision making to find more optimal tradeoffs between the utility of data collected and the burden placed on participants. Wear-IT integrates a variety of consumer-grade sensors and provides adaptive, personalized, and low-burden monitoring and intervention. Proof of concept examples are illustrated using artificial data. The results of qualitative interviews with users are provided. Results: Participants provided positive feedback about the ease of use of studies conducted using the Wear-IT framework. Users expressed positivity about their overall experience with the framework and its utility for balancing burden and excitement about future studies that real-time processing will enable. Conclusions: The Wear-IT framework uses a combination of passive monitoring, real-time processing, and adaptive assessment and intervention to provide a balance between high-quality data collection and low participant burden. The framework presents an opportunity to deploy adaptive assessment and intervention designs that use real-time processing and provides a platform to study and overcome the challenges of long-term mHealth intervention.

  • Untitled. Source: Freepik; Copyright: jcomp; URL:; License: Licensed by JMIR.

    Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning


    Background: Qualitative self- or parent-reports used in assessing children’s behavioral disorders are often inconvenient to collect and can be misleading due to missing information, rater biases, and limited validity. A data-driven approach to quantify behavioral disorders could alleviate these concerns. This study proposes a machine learning approach to identify screams in voice recordings that avoids the need to gather large amounts of clinical data for model training. Objective: The goal of this study is to evaluate if a machine learning model trained only on publicly available audio data sets could be used to detect screaming sounds in audio streams captured in an at-home setting. Methods: Two sets of audio samples were prepared to evaluate the model: a subset of the publicly available AudioSet data set and a set of audio data extracted from the TV show Supernanny, which was chosen for its similarity to clinical data. Scream events were manually annotated for the Supernanny data, and existing annotations were refined for the AudioSet data. Audio feature extraction was performed with a convolutional neural network pretrained on AudioSet. A gradient-boosted tree model was trained and cross-validated for scream classification on the AudioSet data and then validated independently on the Supernanny audio. Results: On the held-out AudioSet clips, the model achieved a receiver operating characteristic (ROC)–area under the curve (AUC) of 0.86. The same model applied to three full episodes of Supernanny audio achieved an ROC-AUC of 0.95 and an average precision (positive predictive value) of 42% despite screams only making up 1.3% (n=92/7166 seconds) of the total run time. Conclusions: These results suggest that a scream-detection model trained with publicly available data could be valuable for monitoring clinical recordings and identifying tantrums as opposed to depending on collecting costly privacy-protected clinical data for model training.

  • Real time medication monitoring. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Technical and Psychosocial Challenges of mHealth Usage for Antiretroviral Therapy Adherence Among People Living With HIV in a Resource-Limited Setting: Case...


    Background: Mobile communication has been found to improve antiretroviral therapy (ART) adherence among people living with HIV. In an ongoing randomized clinical trial, 2 mobile communication strategies (ie, sending SMS text messages and real-time medication monitoring [RTMM]) were used to improve adherence to ART among people living with HIV in Tanzania. We noticed remarkable discrepancies between self-reported adherence and adherence recorded by SMS text messaging or RTMM among some of the first trial participants. Objective: Our objective was to describe these cases and the observed discrepancies in more detail, to serve as a useful illustration of some of the challenges in using mobile health in resource-limited settings. Methods: In an ongoing randomized trial, adults living with HIV from two HIV treatment centers in Tanzania who were suspected of low levels of adherence were randomly assigned in a 1:1:1 ratio to receive (1) SMS text message reminders, (2) an RTMM device, or (3) no additional intervention to standard HIV care. During bimonthly study visits, the participants self-reported their level of adherence, received feedback about their level of adherence based on SMS text messaging or RTMM, and discussed strategies to overcome adherence problems with nurses providing HIV care. For the purpose of this report, we selected people living with HIV who had completed 5 follow-up visits and consistently reported more than 95% adherence, while SMS text messaging or RTMM recorded lower than 75% adherence. The participants were invited for a short, face-to-face in-depth interview to explore reasons for this discrepancy. Results: At the time of this analysis, 26 participants had completed follow-up. Six of these evidenced the above-mentioned discrepancies, with an average adherence of 46% based on SMS text messaging or RTMM, while self-reported adherence was 98%. Five of these 6 participants insisted that their adherence to ART was good, with 4 reporting that their adherence to properly using the monitoring device was low. Three participants mentioned concerns about involuntary disclosure of HIV status as a main reason for low adherence to using the device. Two participants were still depending on other reminder cues despite receiving SMS text message or RTMM reminders. Poor network coverage caused low adherence in 1 participant. Conclusions: Psychosocial barriers were reported as importantly contributing to low adherence, both with respect to use of ART and proper use of the adherence-monitoring device. This case series illustrates that when introducing new digital adherence monitoring technology, researchers should consider psychosocial barriers and distinguish between adherence to device use and adherence to treatment. Clinical Trial: Pan African Clinical Trials Registry PACTR201712002844286;

  • Source: Freepik; Copyright: jcomp; URL:; License: Licensed by JMIR.

    Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study


    Background: Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. Objective: The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. Methods: A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep (Δvitality) and specific sleep measures. Results: A significant association was identified between total sleep time and Δvitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: –0.026, P=.001). There was no significant association between Δvitality and sleep efficiency or number of awakenings. Conclusions: Total sleep time and sleep onset latency are significantly associated with Δvitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures.

  • Walking with Fitbit. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    A Mind-Body Physical Activity Program for Chronic Pain With or Without a Digital Monitoring Device: Proof-of-Concept Feasibility Randomized Controlled Trial


    Background: Chronic pain is associated with poor physical and emotional functioning. Nonpharmacological interventions can help, but improvements are small and not sustained. Previous clinical trials do not follow recommendations to comprehensively target objectively measured and performance-based physical function in addition to self-reported physical function. Objective: This study aimed to establish feasibility benchmarks and explore improvements in physical (self-reported, performance based, and objectively measured) and emotional function, pain outcomes, and coping through a pilot randomized controlled trial of a mind-body physical activity program (GetActive) with and without a digital monitoring device (GetActive-Fitbit), which were iteratively refined through mixed methods. Methods: Patients with chronic pain were randomized to the GetActive (n=41) or GetActive-Fitbit (n=41) programs, which combine relaxation, cognitive behavioral, and physical restoration skills and were delivered in person. They completed in-person assessments before and after the intervention. Performance-based function was assessed with the 6-min walk test, and step count was measured with an ActiGraph. Results: Feasibility benchmarks (eg, recruitment, acceptability, credibility, therapist adherence, adherence to practice at home, ActiGraph wear, and client satisfaction) were good to excellent and similar in both programs. Within each program, we observed improvement in the 6-min walk test (mean increase=+41 m, SD 41.15; P<.001; effect size of 0.99 SD units for the GetActive group and mean increase=+50 m, SD 58.63; P<.001; effect size of 0.85 SD units for the GetActive-Fitbit group) and self-reported physical function (P=.001; effect size of 0.62 SD units for the GetActive group and P=.02; effect size of 0.38 SD units for the GetActive-Fitbit group). The mean step count increased only among sedentary patients (mean increase=+874 steps for the GetActive group and +867 steps for the GetActive-Fitbit group). Emotional function, pain intensity, pain coping, and mindfulness also improved in both groups. Participants rated themselves as much improved at the end of the program, and those in the GetActive-Fitbit group noted that Fitbit greatly helped with increasing their activity. Conclusions: These preliminary findings support a fully powered efficacy trial of the two programs against an education control group. We present a model for successfully using the Initiative on the Methods, Measurement, and Pain Assessment in Clinical Trials criteria for a comprehensive assessment of physical function and following evidence-based models to maximize feasibility before formal efficacy testing. Trial Registration: NCT03412916;

  • Source: iStock by Getty Images; Copyright: Buenaventuramariano; URL:; License: Licensed by the authors.

    A Digital Smoking Cessation Program for Heavy Drinkers: Pilot Randomized Controlled Trial


    Background: Heavy drinking (HD) is far more common among smokers compared with nonsmokers and interferes with successful smoking cessation. Alcohol-focused smoking cessation interventions delivered by counselors have shown promise, but digital versions of these interventions—which could have far greater population reach—have not yet been tested. Objective: This pilot randomized controlled trial aimed to examine the feasibility, acceptability, and effect sizes of an automated digital smoking cessation program that specifically addresses HD using an interactive web-based intervention with an optional text messaging component. Methods: Participants (83/119, 69.7% female; 98/119, 82.4% white; mean age 38.0 years) were daily smokers recruited on the web from a free automated digital smoking cessation program (, EX) who met the criteria for HD: women drinking 8+ drinks/week or 4+ drinks on any day and men drinking 15+ drinks/week or 5+ drinks on any day. Participants were randomized to receive EX with standard content (EX-S) or an EX with additional content specific to HD (EX-HD). Outcomes were assessed by web-based surveys at 1 and 6 months. Results: Participants reported high satisfaction with the website and the optional text messaging component. Total engagement with both EX-S and EX-HD was modest, with participants visiting the website a median of 2 times, and 52.9% of the participants enrolled to receive text messages. Participants in both the conditions showed substantial, significant reductions in drinking across 6 months of follow-up, with no condition effects observed. Although smoking outcomes tended to favor EX-HD, the condition effects were small and nonsignificant. A significantly smaller proportion of participants in EX-HD reported having a lapse back to smoking when drinking alcohol (7/58, 16%) compared with those in EX-S (18/61, 41%; χ21=6.2; P=.01). Conclusions: This is the first trial to examine a digital smoking cessation program tailored to HD smokers. The results provide some initial evidence that delivering such a program is feasible and may reduce the risk of alcohol-involved smoking lapses. However, increasing engagement in this and other web-based interventions is a crucial challenge to address in future work. Trial Registration: NCT03068611;

  • ORCATECH Pillbox. Source: ORCATECH; Copyright: The Authors / ORCATECH; URL:; License: Creative Commons Attribution (CC-BY).

    Feasibility of In-Home Sensor Monitoring to Detect Mild Cognitive Impairment in Aging Military Veterans: Prospective Observational Study


    Background: Aging military veterans are an important and growing population who are at an elevated risk for developing mild cognitive impairment (MCI) and Alzheimer dementia, which emerge insidiously and progress gradually. Traditional clinic-based assessments are administered infrequently, making these visits less ideal to capture the earliest signals of cognitive and daily functioning decline in older adults. Objective: This study aimed to evaluate the feasibility of a novel ecologically valid assessment approach that integrates passive in-home and mobile technologies to assess instrumental activities of daily living (IADLs) that are not well captured by clinic-based assessment methods in an aging military veteran sample. Methods: Participants included 30 community-dwelling military veterans, classified as healthy controls (mean age 72.8, SD 4.9 years; n=15) or MCI (mean age 74.3, SD 6.0 years; n=15) using the Clinical Dementia Rating Scale. Participants were in relatively good health (mean modified Cumulative Illness Rating Scale score 23.1, SD 2.9) without evidence of depression (mean Geriatrics Depression Scale score 1.3, SD 1.6) or anxiety (mean generalized anxiety disorder questionnaire 1.3, SD 1.3) on self-report measures. Participants were clinically assessed at baseline and 12 months later with health and daily function questionnaires and neuropsychological testing. Daily computer use, medication taking, and physical activity and sleep data were collected via passive computer monitoring software, an instrumented pillbox, and a fitness tracker watch in participants’ environments for 12 months between clinical study visits. Results: Enrollment began in October 2018 and continued until the study groups were filled in January 2019. A total of 201 people called to participate following public posting and focused mailings. Most common exclusionary criteria included nonveteran status 11.4% (23/201), living too far from the study site 9.4% (19/201), and having exclusionary health concerns 17.9% (36/201). Five people have withdrawn from the study: 2 with unanticipated health conditions, 2 living in a vacation home for more than half of the year, and 1 who saw no direct benefit from the research study. At baseline, MCI participants had lower Montreal Cognitive Assessment (P<.001) and higher Functional Activities Questionnaire (P=.04) scores than healthy controls. Over seven months, research personnel visited participants’ homes a total of 73 times for technology maintenance. Technology maintenance visits were more prevalent for MCI participants (P=.04) than healthy controls. Conclusions: Installation and longitudinal deployment of a passive in-home IADL monitoring platform with an older adult military veteran sample was feasible. Knowledge gained from this pilot study will be used to help develop acceptable and effective home-based assessment tools that can be used to passively monitor cognition and daily functioning in older adult samples.

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  • A chatbot-based assessment of employees' mental health: Design process and pilot implementation

    Date Submitted: Jun 21, 2020

    Open Peer Review Period: Jun 21, 2020 - Aug 16, 2020

    Background: Stress, burnout and mental health problems, such as depression and anxiety are common and can significantly impact on workplaces through absenteeism and reduced productivity. To address th...

    Background: Stress, burnout and mental health problems, such as depression and anxiety are common and can significantly impact on workplaces through absenteeism and reduced productivity. To address this issue, organizations must first understand the extent of the difficulties by mapping the mental health of their workforce. Online surveys are a cost-effective and scalable way to do this but typically have low response rates, in part due to a lack of interactivity. Chatbots offer one potential solution, enhancing engagement through simulated natural human conversation and use of interactive features. Objective: To describe the design process and results of a pilot implementation of a chatbot-based assessment of employee mental health within the workplace. Methods: A fully automated and intelligent chatbot (‘Viki’) was developed to evaluate employee risks of suffering from depression (PHQ-9), anxiety (GAD-7), stress (DASS-21), insomnia (ISI), burnout (OLBI) and work-related stressors (JSS). The chatbot uses a conversation style and gamification features including story/theme and feedback to enhance engagement. The chatbot was implemented within a small to medium-sized enterprise (SME) (N=120) in a cross-sectional study. Results: In total, 98 (82%) employees started the assessment, and 77 (79%) completed it. The majority of employees (54/77, 70%) reported a high risk of suffering from work-related stress. Over one-third (26/77, 34%) reported a high risk of suffering from burnout, 21 (27%) from anxiety, 14 (18%) from general stress, 12 (16%) from depression and 7 (9%) from insomnia. Depression, anxiety, and insomnia were strongly correlated with a measure of presenteeism (r between 0.8 and 0.9). Conclusions: A chatbot-based workplace mental health assessment seems to be a highly engaging and effective way to collect anonymized mental health data among employees with response rates comparable to face-to-face interviews. Clinical Trial: N/A

  • Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden

    Date Submitted: May 28, 2020

    Open Peer Review Period: May 28, 2020 - Jul 23, 2020

    Background: Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computati...

    Background: Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare. Objective: In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset. Methods: We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits. Results: We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction. Conclusions: This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.

  • Facebook Depression Displays at Two Time Points: A Content Analysis

    Date Submitted: May 15, 2020

    Open Peer Review Period: May 15, 2020 - Jul 10, 2020

    Background: Depression is a prevalent and problematic mental disorder that often has its onset in adolescence. Despite this, depression screening of adolescents is not comprehensive. To aid in screeni...

    Background: Depression is a prevalent and problematic mental disorder that often has its onset in adolescence. Despite this, depression screening of adolescents is not comprehensive. To aid in screenings, adolescent depression symptoms could be identified by viewing their social media as adolescents may use Facebook to disclose depression symptoms. Objective: To investigate displayed depression symptoms on Facebook at two time points. Methods: Content analysis of one year of Facebook data at two time points: Time 1 as adolescents (17-18 years) and Time 2 as young adults (20-22 years). Content analysis applied diagnostic criteria for depression to identify participants’ displayed depression symptoms. Qualitative and quantitative descriptive data for past 12 months at each time point, and non-parametric tests for comparisons. Results: A total of 78 participants’ Facebook profiles were examined, 51% were male. At Time 1, 48 of the 78 adolescents had a Facebook profile and 53.9% of adolescents displayed depression symptom references, with an average of 9.4 references and 3 symptom types. Common symptom types included sleep difficulties, an example post was “5 naps in a day, phew.” At Time 2, 44.9% of young adults displayed depression symptoms with an average of 4.6 references and 2 symptom types. Common symptom types included depressed mood, with example post “is really truly depressed.” There were no gender differences in prevalence or average number of displays at either time point. Conclusions: Social media may be a valuable approach to observe and understand depression over the emerging adult developmental period.