Clinical and Financial Outcomes Associated With a Workplace Mental Health Program Before and During the COVID-19 Pandemic

Bondar, Julia; Morrow, Cecina Babich; Gueorguieva, Ralitza; Brown, Millard; Hawrilenko, Matt; Krystal, John H.; Corlett, Philip R.; Chekroud, Adam M

doi:10.1001/jamanetworkopen.2022.16349

Cited by 15 publications

(18 citation statements)

References 43 publications

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“…Although no significant effects were found in the ITT analysis, results from the CC analysis indicated that the Calm app reduced weekly costs by US $155.82 over 8 weeks of the intervention. This is consistent with a recently published study reporting that an employer-sponsored mental health program in the workplace was associated with a positive financial return on investment for employers across multiple worksites and industries [ 61 ]. However, this study estimated the cost for mental health services by employees, employers, or insurers and used arbitrary salaries for their analysis.…”

Section: Discussionsupporting

confidence: 92%

Testing the Pragmatic Effectiveness of a Consumer-Based Mindfulness Mobile App in the Workplace: Randomized Controlled Trial

Huberty¹,

Espel‐Huynh²,

Neher³

et al. 2022

JMIR Mhealth Uhealth

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Background Mental health and sleep problems are prevalent in the workforce, corresponding to costly impairment in productivity and increased health care use. Digital mindfulness interventions are efficacious in improving sleep and mental health in the workplace; however, evidence supporting their pragmatic utility, potential for improving productivity, and ability to reduce employer costs is limited. Objective This pragmatic, cluster randomized controlled trial aimed to evaluate the experimental effects of implementing a commercially available mindfulness app—Calm—in employees of a large, multisite employer in the United States. Outcomes included mental health (depression, anxiety, and stress), sleep (insomnia and daytime sleepiness), resilience, productivity impairment (absenteeism, presenteeism, overall work impairment, and non–work activity impairment), and health care use (medical visit frequency). Methods Employees were randomized at the work site to receive either the Calm app intervention or waitlist control. Participants in the Calm intervention group were instructed to use the Calm app for 10 minutes per day for 8 weeks; individuals with elevated baseline insomnia symptoms could opt-in to 6 weeks of sleep coaching. All outcomes were assessed every 2 weeks, with the exception of medical visits (weeks 4 and 8 only). Effects of the Calm intervention on outcomes were evaluated via mixed effects modeling, controlling for relevant baseline characteristics, with fixed effects of the intervention on outcomes assessed at weeks 2, 4, 6, and 8. Models were analyzed via complete-case and intent-to-treat analyses. Results A total of 1029 employees enrolled (n=585 in the Calm intervention group, including 101 who opted-in to sleep coaching, and n=444 in waitlist control). Of them, 192 (n=88 for the Calm intervention group and n=104 for waitlist) completed all 5 assessments. In the complete-case analysis at week 8, employees at sites randomized to the Calm intervention group experienced significant improvements in depression (P=.02), anxiety (P=.01), stress (P<.001), insomnia (P<.001), sleepiness (P<.001), resilience (P=.02), presenteeism (P=.01), overall work impairment (P=.004), and nonwork impairment (P<.001), and reduced medical care visit frequency (P<.001) and productivity impairment costs (P=.01), relative to the waitlist control. In the intent-to-treat analysis at week 8, significant benefits of the intervention were observed for depression (P=.046), anxiety (P=.01), insomnia (P<.001), sleepiness (P<.001), nonwork impairment (P=.04), and medical visit frequency (P<.001). Conclusions The results suggest that the Calm app is an effective workplace intervention for improving mental health, sleep, resilience, and productivity and for reducing medical visits and costs owing to work impairment. Future studies should identify optimal implementation strategies that maximize employee uptake and large-scale implementation success across diverse, geographically dispersed employers. Trial Registration ClinicalTrials.gov NCT05120310; https://clinicaltrials.gov/ct2/show/NCT05120310

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Section: Discussionsupporting

confidence: 92%

Testing the Pragmatic Effectiveness of a Consumer-Based Mindfulness Mobile App in the Workplace: Randomized Controlled Trial

Huberty¹,

Espel‐Huynh²,

Neher³

et al. 2022

JMIR Mhealth Uhealth

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“…Longitudinal validation methods, in which a validation sample is drawn from the same population at a later point in time, may provide a limited but pragmatic path to avoid generalizing from one clinical setting to another. The growth of large mental health care delivery systems provides the opportunity to collect large amounts of data and deploy prediction models in the same setting in which they were developed ( 41 ). This strategy can reduce challenges associated with patient heterogeneity and context-dependence, and also help identify temporal or geographic trends that affect a model’s predictions.…”

Section: Improving Model Generalizabilitymentioning

confidence: 99%

Illusory generalizability of clinical prediction models

Chekroud,

Hawrilenko,

Loho

et al. 2024

Science

Self Cite

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It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model’s success in one or two datasets or clinical contexts. We scrutinized this optimism by examining how well a machine learning model performed across several independent clinical trials of antipsychotic medication for schizophrenia. Models predicted patient outcomes with high accuracy within the trial in which the model was developed but performed no better than chance when applied out-of-sample. Pooling data across trials to predict outcomes in the trial left out did not improve predictions. These results suggest that models predicting treatment outcomes in schizophrenia are highly context-dependent and may have limited generalizability.

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“…Concerning clinical outcomes, researchers investigated care fields such as mental healthcare, musculoskeletal care and diabetes care, with various studies suggesting that digital health interventions are associated with health improvements (Bondar et al , 2022; Wang et al , 2019). For instance, Bailey et al (2020) explored the effect of digital physical therapy in over 10,000 study participants showing a positive relationship between engagement and pain reduction.…”

Section: Current State Of Researchmentioning

confidence: 99%

Healthcare startups compete in new digital spaces: the lure of virtual specialties

Jahn

Bohnet-Joschko

2023

JBS

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Purpose This study aims to explore the rise of virtual specialty care startups and understand how the ventures leverage digital services to create a new market space. Given the high level of competition in an established industry like health care, the authors investigate the business models and competitive strategies of leading virtual specialty care startups with unicorn status (i.e. emerging, current and exited unicorns). Design/methodology/approach The authors performed an analysis of Crunchbase data to examine whether rising virtual care ventures target specialty care. They focused on companies from the USA, Europe and Asia-Pacific. The identified virtual specialty care ventures were investigated in-depth via a multiple case study. By reviewing corporate websites, Crunchbase data and media coverage, the authors analyzed the ventures’ business models and competitive strategies. Findings This study demonstrates that even in established and highly competitive markets such as health care, managers and entrepreneurs can still leverage digital services to unlock new market spaces. The data analysis reveals that virtual care startups target the field of specialty care. They create a new market space by focusing on fast access to services, personalization, measurable outcomes and affordability. The majority of investigated companies pursues a platform-based business model approach. Originality/value In the context of an established industry like health care, this study gives insights into a new generation of virtual specialty care ventures. By investigating the ventures’ business models and competitive strategies, the authors advance a young field of research.

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Clinical and Financial Outcomes Associated With a Workplace Mental Health Program Before and During the COVID-19 Pandemic

Cited by 15 publications

References 43 publications

Testing the Pragmatic Effectiveness of a Consumer-Based Mindfulness Mobile App in the Workplace: Randomized Controlled Trial

Testing the Pragmatic Effectiveness of a Consumer-Based Mindfulness Mobile App in the Workplace: Randomized Controlled Trial

Illusory generalizability of clinical prediction models

Healthcare startups compete in new digital spaces: the lure of virtual specialties

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