Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at, first published .
Lessons and Untapped Potential of Smartphone-Based Physical Activity Interventions for Mental Health: Narrative Review

Lessons and Untapped Potential of Smartphone-Based Physical Activity Interventions for Mental Health: Narrative Review

Lessons and Untapped Potential of Smartphone-Based Physical Activity Interventions for Mental Health: Narrative Review


1Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States

2Department of Psychiatry, Harvard Medical School, Boston, MA, United States

3Department of Psychology, University of Virginia, Charlottesville, VA, United States

Corresponding Author:

Emma C Wolfe, BA

Department of Psychology

University of Virginia

Gilmer Hall

485 McCormick Road

Charlottesville, VA, 22903

United States

Phone: 1 3014665679


Background: Physical activity has well-known and broad health benefits, including antidepressive and anxiolytic effects. However, only approximately half of Americans meet even the minimum exercise recommendations. Individuals with anxiety, depression, or related conditions are even less likely to do so. With the advent of mobile sensors and phones, experts have quickly noted the utility of technology for the enhanced measurement of and intervention for physical activity. In addition to being more accessible than in-person approaches, technology-driven interventions may uniquely engage key mechanisms of behavior change such as self-awareness.

Objective: This study aims to provide a narrative overview and specific recommendations for future research on smartphone-based physical activity interventions for psychological disorders or concerns.

Methods: In this paper, we summarized early efforts to adapt and test smartphone-based or smartphone-supported physical activity interventions for mental health. The included articles described or reported smartphone-delivered or smartphone-supported interventions intended to increase physical activity or reduce sedentary behavior and included an emotional disorder, concern, or symptom as an outcome measure. We attempted to extract details regarding the intervention designs, trial designs, study populations, outcome measures, and inclusion of adaptations specifically for mental health. In taking a narrative lens, we drew attention to the type of work that has been done and used these exemplars to discuss key directions to build on.

Results: To date, most studies have examined mental health outcomes as secondary or exploratory variables largely in the context of managing medical concerns (eg, cancer and diabetes). Few trials have recruited psychiatric populations or explicitly aimed to target psychiatric concerns. Consequently, although there are encouraging signals that smartphone-based physical activity interventions could be feasible, acceptable, and efficacious for individuals with mental illnesses, this remains an underexplored area.

Conclusions: Promising avenues for tailoring validated smartphone-based interventions include adding psychoeducation (eg, the relationship between depression, physical activity, and inactivity), offering psychosocial treatment in parallel (eg, cognitive restructuring), and adding personalized coaching. To conclude, we offer specific recommendations for future research, treatment development, and implementation in this area, which remains open and promising for flexible, highly scalable support.

JMIR Mhealth Uhealth 2024;12:e45860




In the 21st century, anxiety and depression have been among the top 25 causes of global disease burden [1]. The COVID-19 pandemic has only intensified the rising prevalence as well as the personal and societal impacts of these disorders [2,3]. As there are simply not enough mental health professionals to meet this need [4,5], alternative interventions—both preventive and curative—are urgently needed. Targeting physical activity is a clear opportunity. Before the pandemic, more than 1 in 4 adults reported sitting for >8 hours per day [6], and this number rose to >40% during the pandemic [7]. Both of these statistics are likely underestimated [8]. Prolonged sedentary behavior, or extended time spent awake with minimal energy expenditure [9], is associated with more severe anxiety and depression as well as higher odds of developing related disorders [10-14]. In contrast, decades of research have demonstrated that regular physical activity is associated with numerous positive psychological outcomes [15-17]. Cross-sectionally, individuals who engage in regular exercise—a subset of physical activity that involves planned, structured, and repetitive bodily movement intended to improve or maintain fitness [18]—report fewer and less severe symptoms of anxiety and depression [19,20], greater positive affect and well-being [21], less stress [22], and lower rates of anxiety and depressive disorder diagnoses [23-25]. At the individual level, people report feeling better on days when they exercise [26-28].

Exercise as an Intervention for Depression and Anxiety

Prospective data support regular exercise as a potent population-level prevention tool, significantly lowering the risk of developing anxiety and depressive disorders [25,29,30]. Encouragingly, even small amounts of physical activity may have an enormous impact on mental health [31,32]. For example, an estimated 12% of new cases of depression could be prevented if the entire population exercised for just 1 hour per week [33]. In addition, among individuals presenting with diagnosable levels of symptomatology, systematically increasing exercise behavior is therapeutic [33-36].

Exercise appears to enhance emotional flexibility, or a person’s ability to self-regulate under stress [37-40]. Physiologically, individuals who exercise more regularly show faster heart rate recovery following stressors than their peers who exercise less, and individual bouts of exercise can mitigate the hypothalamic-pituitary-adrenal axis, heart rate, and blood pressure reactivity to acute stress [39,41-43]. Physical activity can also increase the production of brain-derived neurotrophic factors, which are neurobiological changes that are understood to increase resilience [44]. These effects are mirrored in reports of exercise bolstering emotional recovery following stressors, enhancing coping self-efficacy, and mitigating the impact of rumination and other emotion regulation deficits on prolonging distress [34,39,42,45,46]. Furthermore, exercise benefits physical health targets that share bidirectional relationships with mental health, such as better sleep and cardiometabolic health [47-51]. Critically, positive treatment effects have been found for directly alleviating anxiety [52-55] and depressive [56-58] disorders as well as related and frequently comorbid conditions such as posttraumatic stress disorder [59] and obsessive-compulsive disorder [60,61]. Importantly, research has replicated the benefits of physical activity (ie, reducing psychiatric symptoms) in samples of people with severe mental illness, such as schizophrenia [62]. Similarly, physical activity and other health-related behaviors (eg, sleep hygiene) are considered to be an integral component of treatment for bipolar disorder [63]. Exercise has also been successfully used to augment the effects of other validated psychosocial treatments such as cognitive behavioral therapy (CBT) [64-67].

Despite the broad knowledge that regular physical activity is physically, cognitively, and emotionally beneficial, only approximately half of Americans meet even the minimum exercise recommendation of 150 minutes per week of moderate-intensity or equivalent physical activity [68]. Individuals with anxiety, depression, or related conditions are even less likely to do so [69,70]. They are also more likely than peers without mental health disorders to exhibit elevated sedentary behavior [71,72]. Thus, although acceptable and efficacious tools exist to help individuals meaningfully change their behavior and improve psychiatric symptoms, there is a large gap between the evidence and real-world implementation. Few clinicians include physical activity as an explicit treatment target or use it as an intervention tool [73-75]. Moreover, the larger barriers to treatment within our health care system remain, including the inaccessibility of treatment due to the acute shortage of qualified clinicians; stigma; and patients’ difficulty with travel, timing, and the cost of regular appointments [76].

Promise of Digital Platforms for Promoting Physical Activity

With the advent of mobile sensors and phones, experts have quickly noted the ability of technology to expand the reach of evidence-based psychiatric care; overcome the aforementioned barriers by providing treatment flexibly; and begin reducing long-standing disparities in treatment access, response, and dropout [77-79]. This could also be an efficient, scalable method of promoting increased physical activity among adults with or at risk of anxiety and depressive disorders.

Digital solutions show strong early benefits for activity measurement and intervention in nonpsychiatric populations. In fact, leveraging technology to measure and increase physical activity was an official recommendation from the National Heart, Lung, and Blood Institute and National Institute on Aging “Influences on sedentary behavior/Interventions to reduce sedentary behavior” joint workshop [80]. First, measuring behavior via mobile sensor is validated and widely used [81-84]. Similarly, people’s tendency to carry their phones with them throughout the day allows for more accurate monitoring of physical activity and related progress. The ease of use and, therefore, precision of such technologies (wearable and smartphone-based sensors) is an important boon for research and treatment as self-report measures of activity typically result in underreporting [81-84].

Second, delivering treatment in part or fully via mobile phones is effective in increasing physical activity in nonpsychiatric populations [85-87]. This parallels broader findings that apps can effectively promote other health behaviors (eg, improved nutrition, smoking cessation, and medication adherence) [88,89]. Currently, 97% of Americans have a mobile phone, and an estimated 85% have a smartphone [90]. Although these numbers are lower in certain populations, such as those with serious mental illness (wherein an estimated 85% own a mobile phone and 60%-70% own a smartphone), the ubiquity of smartphones allows for the promotion of behavior change in real time and with a wider array of individuals [91-94]. Inactivity frequently occurs out of conscious awareness or choice due to people’s attention being fixated elsewhere (eg, watching television or taking the elevator at work). As such, personal devices can unobtrusively enhance awareness of behavior, which itself can promote increased activity [95]. Furthermore, technologies can deliver notifications in the moment to interrupt passive episodes while also providing tools to increase activity when individuals are most likely to take action [96]. Mobile app–based physical activity interventions can also gamify exercise to enhance enjoyment, which is a key mechanism for long-term engagement in physical activity [97,98]. In-the-moment enjoyment not only promotes regular exercise but is also in itself beneficial for mental health, contributing to the success of broader interventions such as behavioral activation. Overall, digital interventions are promising as they are low risk (ie, typically focus on reducing sitting and increasing light activity), can be deployed without a clinician, and can be used in the context of a patient’s daily life. Ultimately, research conducted thus far in the general population supports mobile technologies as valid, accessible, and effective methods of promoting physical activity and reducing sedentary behavior.

Current Objective

Although it is reasonable to extrapolate that physical activity interventions could be implemented via smartphone in a similarly feasible, acceptable, and effective manner in psychiatric populations or for psychiatric targets, this remains an open question. High-quality trials of in-person exercise programs for mental health often unintentionally include components beyond the activity itself that are potentially therapeutic, such as regular, structured, and supervised sessions [65]. In other words, as part of most exercise programs, participants also receive regular social engagement or support, face-to-face time with a professional, instruction and demonstration of target behaviors, and guidance with behavioral scheduling or activation, all of which may enhance the therapeutic benefits of physical activity. However, the remote and asynchronous nature of technology-based interventions may provide a different experience from that of in-person programs, and thus, the impact may also differ. On the other hand, the aforementioned benefits of digital interventions, such as their ability to increase accessibility, lower logistical barriers to engagement, and enhance self-awareness while also promoting behavior change in real time, may boost response and, thus, lead to comparable—or even stronger—effects than face-to-face trials. As a result, it cannot be assumed that face-to-face physical activity programs or digital programs designed for other populations (eg, medical) will translate when delivered via smartphone or to a new population.

In this study, we explored the potential of physical activity interventions, as delivered (at least in part) via smartphone, to improve mental health in psychiatric populations. As this topic remains relatively new, we also considered available evidence on these tools to address mental health symptoms in nonpsychiatric populations. Specifically, we highlighted in which populations these tools have been tested; what outcomes have been evaluated (eg, acceptability, behavior change, and symptom change); and how strategies and tools have (or have not) been tailored to individuals with depression, anxiety, or related concerns. The goal was to provide a narrative overview and specific recommendations for future research on smartphone-based physical activity interventions for psychological disorders or concerns.

Literature Search

To provide a narrative overview of this emerging research area, we searched for articles that (1) described or reported an intervention intended to increase physical activity or reduce sedentary behavior; (2) included an emotional disorder, concern, or symptom as an outcome measure; (3) described or reported an intervention delivered entirely or in part via a smartphone app; and (4) were published in English and in peer-reviewed journals. Example search terms include “smartphone,” “smartphone application,” “mobile application,” “mobile app,” “digital mental health,” “app-based,” “app-assisted,” “mobile phone,” “ehealth,” “digital,” “mobile,” “exercise,” “physical activity,” “sedentary,” “sedentary behavior,” “physical inactivity,” “depression,” “dysthymia,” “mood,” “anxiety,” “phobia,” “trauma,” “posttraumatic stress,” “obsessive compulsive disorder,” “post-traumatic stress,” “obsessive-compulsive disorder,” “stress,” “emotional disorder,” “emotional problem,” “well-being,” “wellness,” “affective disorder,” “OCD,” “PTSD,” “MDD,” “GAD,” “mental health,” and “mental illness.” Web-based database (PubMed, Google Scholar, and Cochrane) searches and additional manual searches (eg, searching the reference sections of articles identified through database searches) were conducted up to March 2022. Records were initially reviewed by one coauthor; in cases of uncertainty about appropriateness for this review, records were reviewed in full by 2 additional coauthors and discussed until a consensus was reached.

Data Review

We attempted to extract the following information, where available, from each paper: sample size, inclusion criteria, demographics of the sample, primary aim, trial design (eg, randomized controlled trial), treatment duration, technology used, other interventions used (ie, in addition to physical activity), outcome measures (eg, primary and secondary measures of physical activity), results, treatment components or behavior change strategies, adaptations for mental health, and inclusion of coaching.


In taking a narrative lens, we drew attention to the type of work that has been done and used these exemplars to discuss key directions to build on. Table 1 provides a summary of the included articles.

Table 1. Summary of studies investigating the impact of digital physical activity interventions on mental health symptoms.
Study, yearSample size, NPopulation studiedIntervention usedDurationPhysical activity outcomePrimary psychiatric outcomePrimary medical outcome
Aguilera et al [99], 2020aN/AbAdults with diabetes and a score of >5 on the PHQcApps: DIAMANTE to track data and deliver adaptive learning algorithm (active only) and HealthySMS to send messages (active and control)6 monthsStepsDepressive symptoms (PHQ-8d)HbA1ce levels (blood glucose)
Broers et al [100], 2019557Adults diagnosed with hypertension, symptomatic heart failure, or coronary artery diseaseWearable (Fitbit) activity tracker, wearable (Beddit) sleep tracker, app (Moves) GPS tracker, app (Careportal) home monitoring system6 monthsSteps; physical activity level (combined length of active periods and step count)Anxiety (GAD-7f) and depression (PHQ-9g)N/A
Damschroder et al [101], 2020357Web-based confirmation of veteran statusApp (Stay Strong) and wearable (Fitbit Charge 2)12 monthsActive minutes per week; stepsN/AN/A
Edney et al [102], 2020444Adults currently completing <150 minutes of MVPAh per weekApp (Active Team) and wearables (pedometer and Zencro TW64S)3 monthsDaily minutes of MVPASymptoms of anxiety, depression, and stress (DASS-Di)N/A
García-Estela et al [103], 2021aN/ASpanish-speaking adults with MADRSj score of >1App (IDEApp) and wearable (smartband)8 monthsSIMPAQk; functional exercise capacity (6MWTl and 1-min sit-to-stand test); short Borg CR-10m ScaleDepressive symptoms (PHQ-9); well-being (WHO-5n)Global functioning (SF-36v2o)
Guo et al [104], 2020300Adults who are HIV-seropositive with elevated depressive symptomsApp (WeChat) and Run4Love program (adapted CBSMp course and physical activity promotion) delivered through the WeChat app3 monthsChinese version of the GPAQqDepressive symptoms (CES-Dr)N/A
Haufe et al [105], 2020314Adults with metabolic syndromeWearable (activity monitor; Forerunner 35; Garmin)6 monthsFreiburger Questionnaire on Physical Activity; stepsAnxiety severity and depression severity (HADSs)Change in metabolic syndrome severity; health-related quality of life (SF-36t)
Kim et al [106], 202121Adults aged >46 years diagnosed with PDu or atypical parkinsonism conditions and regular participation in a PD exercise program at least once a weekApp (researcher-created physical activity app)8 weeksTotal exercise calculated by multiplying the frequency and duration for all exercises; subjective exercise scale (Borg 6-20 scale); IPAQvDepression (Geriatric Depression Scale–Short Form)PDQ-39w
Lin et al [107], 2020aN/APhysically inactive adults with a musculoskeletal diagnosis (ICD-10x) and in rehabilitation following inpatient clinic treatmentApp (MoVo)12 monthsBSAy, sport activity and movement activity subscalesDepression (PHQ-9); anxiety (GAD-7)Brief Pain Inventory
Ma et al [108], 2015aN/AAdult participants who were obese and experiencing depressionWearable (Fitbit) and app or website (MyFitnessPal)12 monthsMinutes of physical activity logged on MyFitnessPalDepression (SCL-20z)Changes in BMI
Nadal et al [109], 2021aN/AAdult users with mild to moderate depression who were assigned to iCBTaa treatment for depressionWearable (smartwatch; Mood Monitor watch app)8 weeksSmartwatch activity dataDepression (PHQ-9), anxiety (GAD-7), and functional impairment (WSASab)N/A
Park et al [110], 202160Adults with a history of cardiovascular disease who were within 2 weeks of completing cardiac rehabilitationWearable (Fitbit Charge 2) and apps (Movn and Fitbit)2 monthsSteps; 6MWT; self-reported physical activityQuality of life (QLESQac); depression (PHQ-9)N/A
Puszkiewicz et al [111], 201613Adults with a diagnosis of breast, prostate, or colorectal cancer who had finished primary curative treatmentApp (GAINFitness)6 weeksPhysical activity (GLTEQad)Health and quality of life outcomes (EQ-5D); well-being (FACT-Gae); anxiety and depression (HADS)Cancer-related fatigue (FACITaf); sleep quality (PSQIag)
Puterman et al [112], 2021334Adults with a score of 1-3 on the L-CATah who were cleared to exerciseApp (Down Dog suite of apps—HIITai and yoga)6 weeksSessions of yoga or HIIT completed; minutes of yoga or HIIT completedDepressive symptoms (CESD)N/A
Skrepnik et al [113], 2017172Adults with osteoarthritis eligible to receive the hylan G-F 20 injectionWearable (Jawbone UP24) and app (OA GO)90 daysStepsVAMSajChanges in sleep captured by the wearable activity monitor
Stephens et al [114], 202215Youth (aged 11 to ≤18 years) with MSak and a disability rating of <4 on the EDSSal and attending a pediatric MS and neuroinflammatory disorder clinicApp (Atomic)12 weeksPhysical activity measured via accelerometry; time spent in MVPA and sedentary activities; aerobic fitness, musculoskeletal strength, and walking enduranceDepression (CES-DCam)N/A
Teychenne et al [115], 202162Mothers 3-9 months post partum, insufficiently active, and experiencing heightened depressive symptomsApp (smartphone app and web forum)12 weeksSelf-reported physical activity; accelerometer-assessed physical activity and sedentary behaviorDepressive and anxiety symptoms (unstandardized questionnaires)N/A
Wilczynska et al [116], 2020N/AAdults with or at risk of type 2 diabetesApp (eCoFIT)20 weeksN/ADepressive and anxiety symptoms (PHQ-9 and GAD-7)Social support, self-efficacy, nature relatedness, and perceived sleep quality
Wong et al [117], 202179Adults with moderate depressive symptomsApp (Lifestyle Hubl)8 weeksPhysical activity level (IPAQ)Depressive and anxiety symptoms (PHQ-9 and GAD-7)Insomnia (ISIan); health-related quality of life; health-promoting behaviors (HPLP-IIao); functional impairment (SDSap)

aPublished protocol.

bN/A: not applicable.

cPHQ: Patient Health Questionnaire.

dPHQ-8: Patient Health Questionnaire-8.

eHbA1c: hemoglobin A1c.

fGAD-7: Generalized Anxiety Disorder-7.

gPHQ-9: Patient Health Questionnaire-9.

hMVPA: moderate to vigorous physical activity.

iDASS-D: Depression Anxiety Stress Scales: Depression Subscale.

jMADRS: Montgomery–Åsberg Depression Rating Scale.

kSIMPAQ: Simple Physical Activity Questionnaire.

l6MWT: 6-Minute Walk Test.

mCR-10: Borg Category-Ratio scale.

nWHO-5: World Health Organisation–Five Well-Being Index.

oSF-36v2: 36-Item Short Form Health Survey version 2.

pCBSM: cognitive behavioral stress management.

qGPAQ: Global Physical Activity Questionnaire.

rCES-D: Center for Epidemiologic Studies Depression Scale.

sHADS: Hospital Anxiety and Depression Scale.

tSF-36: 36-Item Short Form Health Survey.

uPD: Parkinson disease.

vIPAQ: International Physical Activity Questionnaire.

wPDQ-39: Parkinson Disease Questionnaire-39.

xCD-10: International Statistical Classification of Diseases, Tenth RevisionI.

yBSA: Movement and Sport Activity Questionnaire.

zSCL-20: Symptom Checklist Depression Scale.

aaiCBT: internet-based cognitive behavioral therapy.

abWSAS: Work and Social Adjustment Scale.

acQLESQ: Quality of Life Enjoyment and Satisfaction Questionnaire.

adGLTEQ: Godin Leisure-Time Exercise Questionnaire.

aeFACT-G: Functional Assessment of Cancer Therapy–General.

afFACIT: Functional Assessment of Chronic Illness Therapy.

agPSQI: Pittsburgh Sleep Quality Index.

ahL-CAT: Stanford Leisure-Time Categorical Activity Item.

aiHIIT: high-intensity interval training.

ajVAMS: visual analog mood scale.

akMS: multiple sclerosis.

alEDSS: Expanded Disability Status Scale.

amCES-DC: Center for Epidemiologic Studies Depression Scale for Children.

anISI: Insomnia Severity Index.

aoHPLP-II: Health-Promoting Lifestyle Profile-II.

apSDS: Sheehan Disability Scale.

Who Was Included in This Work?

Overall, a review of the literature revealed that little work has been done to test the impact of smartphone-based physical activity interventions on increasing physical activity or reducing mental health symptoms in psychiatric or at-risk populations. Most related trials with clinical populations have been conducted in the area of medicine, with studies investigating the effects of physical activity—encouraged through smartphone- and wearable-based interventions—on physical health conditions (eg, diabetes [99,116], obesity [86,108], cancer [111,118], cardiovascular issues [100,110], and multiple sclerosis [114]). Physical activity is well established as a means of facilitating rehabilitation following serious illness or injury, as well as mitigating the progression of chronic health conditions [119-121]. For example, adults with Parkinson disease who used a minimally supported, customizable home-based exercise app for 8 weeks doubled their amount of weekly exercise (minutes) while also increasing the intensity of such exercise [106]. Similarly, engagement with smartphone-based physical activity interventions led to increased strenuous exercise among adults with cancer [111] as well as increased step count for those with cardiac issues [100,110] and for youths with multiple sclerosis [114]. These changes are noteworthy as medical illness or disease can serve as a barrier to engaging in health-promoting behaviors [122] despite the knowledge that such behaviors can stabilize or even improve such medical conditions [123].

In contrast, the practice of formally integrating exercise into mental health care is relatively new and not established in current standards of care. This is mirrored by the disproportion of extant research examining digital tools for increasing physical activity in medical versus psychiatric populations. When mental health targets were examined, they were largely included as secondary or exploratory outcomes and frequently framed in relation to coping with the medical concern of interest [99,100,104-108,110,111,113,114]. We only identified 16% (3/19) of the studies that specifically recruited individuals with psychiatric symptoms, and all (3/3, 100%) were focused on individuals with depressive symptoms [103,109,117]. The most common mental health outcomes were depression, anxiety, general quality of life, and emotional well-being [99,100,102-110,112,114-117]. Specifically, subclinical depressive concerns were the most frequently investigated psychiatric target, followed by subclinical anxiety [100-105,112,115-117].

The impact of smartphone-based physical activity interventions is yet to be investigated explicitly for individuals diagnosed with depressive or anxiety disorders, let alone other mental health conditions, including serious mental illness. Furthermore, although wide age brackets were represented across the studies, with average ages ranging from teenagers to older adults, men and non-White individuals were underrepresented. As research progresses in this space, it will be imperative to include the experiences and perspectives of adults with clinical levels of psychiatric concerns as well as diverse backgrounds and identities.

Do Smartphone-Based Physical Activity Interventions Benefit Mental Health?

Owing to the limited available data; small samples comprising mostly White Western, educated, industrialized, rich, and Democratic women with subclinical depression or anxiety; and heterogeneity of outcomes measured, it is difficult to conclude whether and to what extent existing smartphone-based physical activity interventions benefit mental health. However, with these caveats in mind, we aimed to synthesize the available evidence in the following sections.

Feasibility and Acceptability

There is encouraging evidence that smartphone-based physical activity interventions could be feasible and acceptable for psychiatric populations. However, supporting data were primarily collected in samples of individuals with elevated depressive symptoms or who were at risk of depression rather than in explicitly clinical samples or among individuals with other prominent mental health concerns. One study of postnatal women at risk of depression found low engagement with the digital aspects of a 12-week multicomponent physical activity intervention (home exercise equipment and a physical logbook combined with a motivational smartphone app and a web-based social support forum) [115]; however, other studies reported more positive participant response and engagement. For example, retention in smartphone interventions for physical activity tended to be high compared to that in other types of digital health interventions—one systematic review found that completion rates of digital mental health interventions ranged from 1% to 28% [124]. In a study of adults with diabetes, retention in a 20-week digital physical activity intervention (an app that allowed participants to use workout circuits, set goals, monitor progress, and learn cognitive and behavioral strategies) was of >70% [99]. Similarly, compliance with a suite of high-intensity interval training (HIIT) and yoga apps during a 6-week intervention was strong in a community sample with elevated depressive symptoms. More than half of the participants in the yoga and HIIT+yoga group and 40% in the HIIT group continued completing the recommended 4 sessions per week by the end of the trial [112]. Compliance and satisfaction ratings were comparably high when physical activity promotion was combined with cognitive behavioral stress management via a WeChat intervention in a group of adults with HIV and elevated depressive symptoms [104].

These findings are consistent with those of the larger literature showing that digital physical activity interventions tend to be well received by participants [85,125,126]. Public interest is already high, with physical activity and fitness apps dominating the mobile health space. Notably, a 2018 systematic review of the experience of adults who used mobile interventions to promote physical activity highlighted important themes to be considered for future design—self-reported engagement was most enhanced by the availability of social features, prompts, goal setting, personalization or customization, and gamification but was limited by low technological literacy, preference for coached apps, and a desire for social support [127].

Change in Psychiatric Symptoms

The impact of smartphone-based physical activity interventions on psychiatric symptoms was far more mixed. Some studies observed resultant improvements in symptoms. In one study that included patients specifically recruited for having elevated symptoms of depression and anxiety, patients experienced a reduction in depression and anxiety scores following a 6-month exercise intervention (150 minutes of moderate physical activity per week with individual recommendations given via a smartphone app) as compared to a waitlist control. However, it is notable that this intervention did not test a smartphone-based physical activity intervention in isolation but, instead, combined it with nutritional counseling and the option of receiving exercise recommendations through personal meetings or by phone instead of an app [105]. Among adults from the general population with low physical activity scores, using a publicly available suite of exercise apps for 6 weeks significantly improved depressive symptoms compared to a waitlist control [112]. Psychiatric improvements were also observed in patients with medical comorbidities. For example, in a study of adults with obesity using a smartphone-based physical activity app (eCoFIT), depression symptom severity improved after 20 weeks [116]. Finally, in a study of older adults with Parkinson disease, using a mobile app to access and customize a home-based exercise program for 8 weeks led to reduced depression symptoms and improved quality of life [106].

However, other studies reported null effects. For example, among patients with a history of cardiovascular disease, a smartphone-based intervention including motivational prompts and educational messages did not yield significant changes in depressive symptoms from baseline to 2 months [110]. Furthermore, a trial of patients with cancer using a tailored physical activity smartphone app (which included workout videos, spoken instructions, and push notifications) did not observe changes in depression, anxiety, or quality of life after 6 weeks [111]. In a study testing an app-based physical activity program for youth with multiple sclerosis (including personalized coaching and promotion of aerobic fitness, musculoskeletal strength, and walking endurance), there was no change in depression levels over 12 weeks [114]. Furthermore, a meta-analysis of studies examining digital physical activity interventions in cancer survivors found that none of the included studies were successful in improving depression or anxiety [118]. In the general population, one study similarly did not find significant differences in depression, anxiety, stress, or well-being after 3 or 9 months of using an app (Active Team) and wearable pedometer [102].

These results should be interpreted cautiously for 2 reasons. First, there is the confound of potential floor effects—as none of these trials were designed to address questions about mental health, symptom levels at study start were typically already low, thus reducing investigators’ abilities to identify possible effects. In addition, studies varied widely in their evidence of behavior change, including outcome measures (eg, minutes of activity, number of sessions completed, exercise intensity, and fitness level; see Table 1 for detailed information on this variance) and the use of objective versus subjective reports, which are known to be discrepant [82-84]. In other words, if an intervention did not produce meaningful physical activity changes, it would be unlikely that downstream emotional changes would occur.

How Have Smartphone-Based Physical Activity Interventions Been Tailored to Individuals With Depression, Anxiety, or Other Psychiatric Concerns?

To date, interventions generally have not been tailored to the specific needs or presentations of individuals with mental health concerns. This is unsurprising as most technology-based physical activity trials have not been designed to target mental health. However, some studies have integrated components that specifically address psychological well-being. One adaptation that is low effort but high return is adding psychoeducation about the relationship between physical activity and mental health. For example, a recent trial for adults with mild to moderate depressive symptoms devoted the first in-person group session to discuss the relationship between depression and exercise to complement the personalized exercise program, smartphone app, and wearable device they received [103].

The second adaptation observed in the literature is offering concurrent psychotherapy-based tools. In some cases, psychotherapeutic content was interspersed with the physical activity intervention; for example, in a trial for adults living with HIV and depression in China, both an exercise promotion intervention and cognitive behavioral stress management course were delivered as multimedia messages through the WeChat app [104]. Relatedly, in a study of the eCoFit app for adults with or at risk of type 2 diabetes, short cognitive behavioral tasks (“FitMind Challenges”) were integrated throughout the program [116]. Examples of FitMind Challenges included motivational strategies, relaxation, cognitive restructuring, social support, and problem-solving. In other cases, the approaches were delivered in parallel. In one trial, adults who were overweight and experiencing depression received a 7-step problem-solving therapy via a workbook in addition to live lifestyle coaching, at-home video lessons, the MyFitnessPal app, and a Fitbit for monitoring [108]. In another study, SilverCloud’s guided internet-based CBT program for depression was the primary intervention, with smartwatch-based monitoring of sleep, steps, and mood added to promote greater awareness of the relationship between health behaviors and mood, thereby independently encouraging positive lifestyle changes [109].

A third but largely unexplored avenue is the inclusion of personalized or tailored messaging. This is an opportunity for coaches or other support persons to address barriers that may be specific to the experience of someone with mental health concerns (eg, navigating social anxiety to go to the gym and restructuring depressive thoughts). In one open trial of a physical activity app for youth with multiple sclerosis, coaches were trained in social cognitive theory for behavior change as well as motivational interviewing [114].

Although few digital physical activity interventions have been designed or modified to specifically affect mental health, many have been designed using evidence- and theory-based behavior change strategies that are ripe for implementation in psychiatric contexts. Indeed, the most successful interventions are based on behavioral theory [73,127]—explicitly stated or not—such as the transtheoretical model [128], the theory of planned behavior [129], self-determination theory [130], and social cognitive theory [131]. Social cognitive theory is most often cited given its emphasis on internal, external, and social factors that reinforce learning and contribute to sustained change [131]. Targeting self-efficacy, self-regulation, and social support to engender meaningful, lasting behavior change aligns strongly with principles of psychotherapy as well. Digital physical activity interventions have also experimented with numerous evidence-based behavior change techniques, including goal setting and review, action planning, regular feedback, self-monitoring of behavior, instruction and demonstration of how to perform a new behavior, graded tasks, prompts and cues, and social rewards, to name a few [132-134]. Interventions integrating multiple behavior change strategies are more successful than those that rely on one (eg, self-monitoring or reminders alone [133]). Considering how such strategies could be adapted for individual presentations (eg, those with clinical levels of dysregulation) should be a priority for future iterations of these programs.

Furthermore, technology-driven techniques may uniquely (or at least more strongly than traditional treatments) engage key mechanisms of behavior change. For example, these tools can promote self-awareness. As people tend to keep their devices close to them throughout their daily lives, wearable and mobile platforms can provide objective, continuous monitoring and feedback related to behavioral patterns such as physical activity [102,108]. In addition, these approaches can enhance a person’s likelihood of changing their behavior by lowering the cognitive burden involved in initiating physical activity. Strategies include delivering content more flexibly (eg, when it is most convenient for a participant to engage or in doses of their choosing); modeling target behavior via written, image, or video instructions that can be reviewed on demand or infinite times; or tailoring activity suggestions to a person’s present context (eg, suggesting at-home activities on rainy days). This may be particularly meaningful for psychiatric audiences as depression and anxiety are associated with attention and memory deficits that can interfere with information processing and learning [135-138]. In addition, in-the-moment rewards and other gamification or reinforcement features could be particularly useful early on [139,140] as individuals with depression and anxiety may not experience initial sessions of exercise as intrinsically gratifying or mood boosting as others do; for example, depression is characterized by deficits in reward processing and motivation [141], and anxiety sensitivity and social anxiety can blunt positive responses or promote avoidance [142,143].

Finally, personal devices may allow for more consistent, flexible social support throughout an intervention. Social support is an established, evidence-based behavior change technique that promotes physical activity [126,144-146]. Smartphone-based physical activity interventions provide a range of avenues for social connection, such as texting with a coach [101,107], access to an web or app-based discussion forum [115], and creation of virtual “teams” [102]. Critically, although social media has been frequently incorporated as a means of facilitating connection, participant reactions have been mixed, and it may not be optimal for psychiatric populations [126]. In general, social support appears to boost engagement when it is perceived to facilitate emotional support, provide tips from peers, enhance motivation, foster social comparison or competition [126,147,148]. How to best leverage social support and social media for psychiatric populations requires nuanced future study.

Principal Findings

The primary aim of this narrative synthesis was to examine the status of smartphone-based physical activity interventions for mental health and understand how they have and have not been tailored to or evaluated in psychiatric populations. Ultimately, the literature is limited and difficult to synthesize owing to the high heterogeneity across the studies in terms of sample selection; study design; outcomes of feasibility, acceptability, and efficacy; and degree of tailoring. To date, mental health outcomes have typically been secondary or exploratory within trials focused on medical outcomes (eg, diabetes management) and, when included, have had a narrow focus on measures of depression, anxiety, and general well-being in nonclinical populations. As a result, this review relied significantly on research focusing on medical populations to explore how smartphone-based physical activity interventions could be used to impact mental health outcomes and to infer how they may be used in psychiatric populations. Furthermore, although extant studies have included diversity of age, the samples in the included studies comprised mostly White and female individuals, thus reducing the generalizability of the already limited findings.

The feasibility and acceptability of these interventions for subclinical and at-risk populations are encouraging and suggest that digital physical activity programs may be similarly well received among individuals above diagnostic thresholds. The available data on psychiatric outcomes were mixed. However, it is difficult to draw meaningful conclusions given the limited data; high heterogeneity of intervention approach and target behavior; and lack of standardization in measurement and reporting of use, engagement, and behavior change, as well as the elevated risk of floor effects given the subclinical samples. These inconclusive psychiatric outcomes may also be related to a lack of tailoring of smartphone-based physical activity interventions to the specific needs of those presenting with mental health concerns. The existing tailoring included basic psychoeducation about physical activity as a treatment, adding concurrent psychotherapy-based tools, and including personalized or tailored messages. There was no standardization or evidence base for how this tailoring was applied. The upshot is that many of the papers included in this review presented interventions that were already built around established, evidence-based behavior change strategies, which suggests that psychotherapeutic tailoring could be efficiently integrated into existing smartphone-delivered physical activity interventions. In general, effective physical activity interventions use many of the same fundamental behavior change strategies commonly found in psychotherapy, such as education, goal setting, self-monitoring, graded tasks, engaging social support, and motivational interviewing [149,150].

Taken together, the primary barrier to advancing the use of smartphone-based physical activity interventions in mental health care is the absence of evidence. The need for research in this area has been highlighted in other reviews as well [151,152]. To construct a more consistent, evidence-based foundation for intervention development, we outline several avenues for future research.

Recommendations for Tailoring Physical Activity Interventions to Psychiatric Populations

More research is needed to better understand how existing smartphone interventions can be tailored to fit the needs of psychiatric populations. The following are example adaptations rather than an exhaustive list. One likely critical step is to provide users with psychoeducation early on about the ways in which physical activity can be used to affect psychological health, such as improving mood and reducing anxiety. This should involve making explicit connections between health behaviors (eg, exercise), mental health symptoms, and emotion regulation so that users can better appreciate the bidirectional links between these areas of well-being. Including even a brief text summary of the literature or treatment rationale could likely augment the effects [153,154]. In fact, there is evidence with depression treatment that physical activity interventions lacking such psychoeducation or treatment rationale do not lead to robust clinical changes and can worsen dropout rates [155]. In contrast, attending to the mental and emotional benefits of exercise, particularly the acute or immediate impact on affect or resilience, can further enhance mood and motivation to continue exercising [35,156]. Highlighting these benefits and encouraging users to monitor such positive changes could improve sustained engagement and clinical response. Technology may be particularly helpful for this; apps, for example, can provide in-the-moment reminders through push notifications to attend to one’s affect or visual feedback of a user’s pretest-posttest change in self-reported mood with exercise. Furthermore, digital tools could provide information about the impact that mental health symptoms may have on program engagement. This can help users recognize that it is normal and expected for symptoms such as fatigue, anxiety sensitivity, or low motivation to serve as barriers to physical exercise and can proactively help users engage in related problem-solving.

Another compelling feature to test is the incorporation of modules or content that specifically address mental health concerns or symptoms. For example, technology-based physical activity interventions aimed at improving anxiety symptoms would benefit from including evidence-based skills such as cognitive restructuring and exposure practices. These approaches can be used to identify and challenge maladaptive beliefs that anxiety symptoms such as a racing heart are dangerous (known as anxiety sensitivity [157]), design a more graded exercise plan, and use activity as an interoceptive exposure by allowing patients to experience and tolerate those feared sensations [158]. Furthermore, CBT skills can be incorporated to address exercise-related social anxiety, such as integrating exposures (eg, walking with a friend or going to a gym first at off-peak hours) and challenging associated negative expectations (eg, “I won’t be able to keep up with my friend and they will judge me”). Meanwhile, a digital physical activity intervention aimed at helping people with depression could include skills consistent with behavioral activation, such as tracking the relationship between mood and activities (including physical activity); intentionally adding new behaviors such as gardening, walking, or going to the gym to their weekly schedule; and generating more flexible approaches to regular movement. Furthermore, cognitive skills can be used for participants to identify and evaluate negative thoughts about themselves or the program (eg, “I can never stick to my goal of going for walks, so what’s the point”) in terms of their accuracy or utility. In this vein, equipping coaches with some knowledge of common mental health symptoms to look out for, destigmatize, and address could enhance outcomes.

In addition, coaches are understood to bolster digital interventions in general by providing further psychoeducation or resources, personalizing content or skill use, and answering questions. Given the known relationships between mental illness and both inactivity and chronic medical conditions, even with basic mental health knowledge, coaches could perform these roles better. It is also important to recognize that digital or physical activity interventions may not be the appropriate or most effective level or type of care for all individuals experiencing mental health concerns. As such, tools should include information for users on the signs or symptoms that may indicate that pursuing psychotherapy could be beneficial as well as resources for doing so.

Finally, physical activity promotion tools could be integrated to augment existing treatments. Currently, there are a number of well-established treatments for psychiatric disorders, such as CBT and mindfulness. CBT has extensive research support as the gold-standard treatment for a range of disorders, including depression and anxiety [159]. This treatment integrates both behavioral and cognitive skills, such as tracking and scheduling activities as well as evaluating and challenging maladaptive thoughts, to reduce the severity and impact of symptoms. Mindfulness—or the purposeful, nonjudgmental awareness of the present moment [160]—is increasingly included in “third wave” interventions to reduce psychiatric symptoms [161] as well as increase well-being, such as positive affect and quality of life [162,163]. Recently, a dominant focus of digital mental health innovation has been translating these gold-standard psychotherapies to digital platforms. There is now a strong foundation of evidence supporting the feasibility, acceptability, and efficacy of delivering these treatments through both face-to-face and digital means [164-166].

Thus far, the development of digital interventions for physical activity and for mental health has largely occurred separately. However, their concurrent delivery provides promising initial evidence [111,118]. This parallels in-person trials demonstrating that increasing physical activity strengthens psychotherapy outcomes [167,168]. The next step in this line of research is to more formally integrate the 2 or even develop technologies in which both sets of skills are delivered within a single coherent platform. For example, in a study conducted by Wilczynska et al [116], adults with diabetes used the eCoFit app, which integrated guided workouts, goal setting, and cognitive behavioral skills. Some of the commercially available apps targeting mental health have already begun moving in this direction as well. The mindfulness-based app Headspace has recently incorporated a suite of video- and audio-guided exercises that help users engage in activities such as stretching, dancing, and yoga. Within these integrated platforms, it will be important to explicitly link the mental health and physical activity content rather than presenting them side by side as distinct intervention pathways.

Recommendations for Developing and Testing Physical Activity Interventions for Populations With Psychiatric Disorders


More research is also needed to understand to what degree smartphone interventions require tailoring and for whom. Given the wide range of possibilities, an important step in the development process is to have focus groups with the goal of hearing from individuals with lived experience about their wants and needs. Through pilot-testing, intervention design and refinement can be an iterative process wherein individuals of the target audience engage with the program, feedback is elicited, and changes are made in response to that feedback. This user-centered approach fits well within the larger preparation phase of a Multiphase Optimization Strategy. Following such development, digital tools should be scientifically tested and optimized, leading to a randomized controlled trial to examine their efficacy in achieving the outcomes of interest (eg, reduction in depression symptoms). This testing phase is necessary to establish a program as evidence-based, which would allow it to stand out in an otherwise large pool of digital applications that are not backed by research.

Leveraging New Trial Designs

New trial designs, such as sequential multiple-assignment randomized trials, microrandomized trials, and factorial designs, will be useful in intermediate stages to parse issues such as dosing, sequencing, and personalization. For example, research shows that at least 6 weeks are required for new physical activity habits to form [169]; thus, interventions that are, on average, 8 weeks long lead to more lasting changes than shorter ones [169]. Moreover, it remains unclear whether longer treatments, such as ≥24 weeks, have a greater impact on the general population [170]. It is unknown what duration would be sufficient for various clinical populations to observe changes in both the target behavior and in downstream symptoms and for whom extended support would be necessary. Individual components such as the aforementioned tailoring elements or the inclusion of coaching can also be efficiently tested using these new study designs [170].

Understanding the Role of Human Support

Previous work has shown that supervised exercise tends to have a larger impact on anxiety outcomes than unsupervised prescriptions [171]. This parallels guidance from experts in digital mental health that including human support (eg, a lay coach or therapist) alongside internet- or app-based cognitive behavioral and other therapies should enhance retention, engagement, and outcomes. Possible explanations could be greater accountability, the presence of social support, clearer guidelines, opportunities to ask for clarification, in-the-moment personalization, problem-solving, direct affirmation or reinforcement, and a more regular routine. However, guidance does not unilaterally improve outcomes for all digital interventions or all patients [164,172]. There are currently no evidence-based guidelines for implementing human support in digital interventions (ie, when, how often, how much, by whom, and for which users), let alone a nuanced understanding of how coaches can effect positive change in adherence or response [173]. Understanding the mechanisms of action would allow developers to maximize automation and most efficiently deploy human support when needed. As human support is the most expensive and scarce resource in digital health solutions, it will be critical to determine how to automate some of these supportive pathways and how to most efficiently identify who needs human support and at what dose.

Considering Individual Factors

Across intervention types, we must also consider the individual factors that may serve as facilitators of or barriers to engagement and success. This is doubly important for smartphone-based physical activity interventions as there are potential barriers inherent in both smartphone use and physical activity uptake.

Barriers to Physical Activity

Research examining barriers to physical activity in those with mental health conditions suggests that individuals with high symptom severity and low self-efficacy may be particularly disinclined to pursue physical activity–based interventions [174,175]. Lack of social support, lack of available time, and fear of injury were also frequently mentioned barriers in a sample of adults with anxiety and depression [174]. In addition, individuals with higher or lower levels of baseline physical activity or fitness may face different barriers and have different needs. A qualitative review suggested that those with lower baseline physical activity wanted an app that had more of a coaching role, whereas those with a higher baseline physical activity preferred an app that helped them intensify or optimize their current physical activity level [126]. In addition, developers should consider the accessibility of exercise suggestions; for example, exercise prescriptions that necessitate equipment, a gym membership, or access to a safe outdoor space may not be generalizable to many otherwise well-suited recipients.

Barriers to Digital Mental Health Use

A recent review by Borghouts et al [176] examining barriers to and facilitators of user engagement found that scoring high on neuroticism and agreeableness was associated with greater interest in using smartphone apps to reduce stress, whereas scoring high on extraversion was a predictor of preferring in-person services to web-based options [177]. The severity of baseline symptoms—both psychiatric and comorbid medical concerns—may also play a role in engagement and adherence. Most smartphone-based digital physical activity interventions have been investigated in those with mild to moderate symptoms, which can hamper engagement with apps [178,179]. Some studies suggest that those with mild depression may actually be at an even greater risk of dropout than those with moderate depression [178,180]. Researchers should also be cautious about potential iatrogenic app components. For example, the tracking components inherent in many smartphone-based physical activity interventions, particularly those related to physical health and tracking activity, run the risk of becoming compulsive or rigid. This could pose an issue for individuals with obsessive-compulsive, anxiety, and related disorders that are often characterized by perfectionism or inflexibility. Furthermore, peer support groups within apps, although often helpful, could also lead to negative social comparisons, thus exacerbating depression.

A possible mitigating approach to this would be to introduce smartphone-based physical activity interventions through a stratified care model in which individuals are allocated to different levels of an intervention depending on their clinical needs. In this model, providers could use patient-level data to decide whether an individual would benefit from the smartphone tool as a stand-alone intervention, as a coached version integrated with another level of care (eg, psychotherapy with a clinician), or delivered after progress with another intervention has been made (eg, medication stabilization).

Barriers Related to Technology

For many, significant barriers may include technology literacy and access. Multiple studies have identified technology literacy as an obstacle to digital physical activity intervention uptake [126,181] and for digital mental health use [178,180]. This, coupled with the lack of technical support provided by many apps, means that individuals who may be motivated to engage with smartphone-based physical activity interventions are stymied by the inability to navigate the app or seek help when issues arise. This necessitates a user interface and navigability features that can be understood or customized by a range of age groups and technological ability levels, and furthermore, it emphasizes the importance of accessible, embedded technical support tools for those who need them. Coached or guided tools may be helpful in mitigating this literacy issue but still require users to have the basic skills needed to contact their coaches or guides for help. In addition, there is still a subset (15%) of the US population that does not own a smartphone, many of whom represent communities that could benefit the most from flexible, low-cost, and accessible support options [182]. An even greater percentage of Americans lack a stable internet connection; this statistic is highly stratified along racial lines—8 in 10 White adults report having a broadband connection at home, whereas only 71% of Black adults and 65% of Hispanic adults report the same [183]. This suggests a need for digital physical activity interventions that can be accessible from communal settings, such as local community centers or publicly available fitness facilities.

It should be acknowledged that the vast majority of digital mental health interventions are designed—intentionally or not—with primarily White, Western, educated, industrialized, rich, and Democratic populations in mind. Research investigating the efficacy of both in-person and digital physical activity interventions also suffers from similarly nonrepresentative samples, calling into question which validated strategies are universally beneficial. For example, there is compelling research that physical activity interventions (both in person [184,185] and virtual [186]) are effective for health behavior change in Black Americans; however, other studies investigating the perspectives of Black Americans suggest that many in this community face unique social and structural barriers to physical activity that may not be considered by extant programs [187,188]. Thus, engagement in smartphone-based physical activity interventions by marginalized populations might be impacted by both the perception and reality that many of these apps are not designed with their community or culture in mind. Future research should engage with a range of underserved populations in qualitative research to understand the community and cultural values surrounding physical activity and technology use. This information should then be used to collaboratively design new programs or features or culturally tailor existing tools to meet the needs of a broader audience.

All these limitations support the importance of qualitative work as a future direction when building smartphone-based physical activity interventions for mental health. The research synthesized by Carter et al [126], for example, presents valuable insights into individual-level concerns and emerging trends in patient preferences for components and design of apps. In particular, their identification of 2 key mechanisms through which mobile health use facilitates physical activity (strengthening motivation and changes in self-awareness and strategizing) is an important step in boosting engagement and exploring the mechanisms by which these apps function. Engaging qualitatively with a broad spectrum of stakeholders would also be foundational in improving the issue of representation, thereby supporting the goal that smartphone-based physical activity interventions for mental health are acceptable and efficacious for all.

Practical Considerations

The unfortunate reality of scientifically validated digital mental health products is that the vast majority do not move beyond their success in the laboratory [173], and those that do make it to market face fierce competition, flagging engagement rates, and a lack of financial means to scale the project. Thus, in developing and testing these promising digital mental health–focused physical activity tools, investigators should integrate elements essential for successful dissemination. One proposed pathway for improving the dissemination and ultimate success of digital mental health tools is to connect consumers through employers or public and private insurance companies, who have indicated a growing interest in expanding services to cover digital mental health. To illustrate, Blue Shield of California is now offering the mindfulness meditation app Headspace to subscribers [189]; Cigna offers the mental health app Ginger as part of its service package [190]; and Kaiser Permanente supports the use of Ginger, Calm, and MyStrength [191]. Physical activity technologies that track and manage exercise and step count are even more prevalently covered by insurance. Blue Cross Blue Shield, United, and others have partnered with Fitbit to offer low-cost wearable devices and use of their apps to promote health behavior change. Aetna and Cigna offer similar programs and occasional incentives to people who use health-tracking apps and devices. Taken together, the enthusiasm for apps and devices promoting physical health, as well as the recent foray by insurers into the digital mental health space, suggests that smartphone-based physical activity interventions for mental health may be prime for scalable coverage. This also means that academics developing such tools should be mindful when designing research studies to collect outcome data relevant to insurers and other payers, such as outcomes related to health care costs (reduction in insurance claims and physician or therapist visits), disability-adjusted life years (reduction in overall illness burden), adoption and engagement rates, and user data such as acceptability and fidelity (whether people use the tools as intended). In addition, investigators and designers should carefully consider the costs inherent to their interventions, such as relying on “off-the-shelf” versus research-grade devices and other platforms, the extent to which an intervention relies on human support to be administered, and the broader infrastructure required for implementation and sustainment, all of which will alter accessibility and scale. By designing research studies on smartphone-based physical activity interventions with true scalability in mind, researchers will be better poised to expand their intervention beyond academia and better achieve the goal of connecting evidence-based interventions with those who need them.


Physical activity has well-known and broad mental health benefits. However, a minority of at-risk individuals or those with mental disorders meet even the minimum exercise recommendations. Smartphones may bridge this gap given their pervasiveness in daily life, capacity to help concurrently manage multiple dimensions of personal health, and ability to engage key mechanisms of behavior change. Although early data for smartphone-based physical activity interventions reducing psychological symptoms are encouraging, overall, surprisingly little work has been done in this area. Therefore, there is untapped potential for developing and disseminating accessible, beneficial tools that can have a great public health impact.


During the preparation of this manuscript, EEB and BMH were supported in part by the Hope Fund, an internal fellowship at Massachusetts General Hospital provided through the generosity of an individual donor to support early-career investigators.

Conflicts of Interest

EEB receives research support from Koa Health, is a presenter for the Massachusetts General Hospital Psychiatry Academy in educational programs supported by independent medical education grants from pharmaceutical companies, and has a consulting agreement with Otsuka Pharmaceutical Development & Commercialization, Inc. ECW has no competing interests to declare. BMH receives research support from Koa Health. SW is a presenter for the Massachusetts General Hospital Psychiatry Academy in educational programs supported by independent medical education grants from pharmaceutical companies and has received royalties from Elsevier Publications, Guilford Publications, New Harbinger Publications, Springer, and Oxford University Press. SW has also received speaking honoraria from various academic institutions and foundations, including the International Obsessive-Compulsive Disorder Foundation, Tourette Association of America, and Centers for Disease Control and Prevention. In addition, she received payment from the Association for Behavioral and Cognitive Therapies for her role as associate editor of the Behavior Therapy journal as well as from John Wiley & Sons, Inc, for her role as associate editor of the Depression & Anxiety journal. SW has also received honoraria from One Mind for her role in the PsyberGuide scientific advisory board. SW is also on the scientific advisory board for Koa Health, Inc, and Noom, Inc. SW has received research and salary support from Koa Health, Inc. In addition, SW has a consulting agreement with Noom, Inc.

  1. GBD 2019 Diseases Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. Oct 17, 2020;396(10258):1204-1222. [FREE Full text] [CrossRef] [Medline]
  2. COVID-19 Mental Disorders Collaborators. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. Nov 06, 2021;398(10312):1700-1712. [FREE Full text] [CrossRef] [Medline]
  3. Twenge JM, Joiner TE. U.S. Census Bureau-assessed prevalence of anxiety and depressive symptoms in 2019 and during the 2020 COVID-19 pandemic. Depress Anxiety. Oct 2020;37(10):954-956. [FREE Full text] [CrossRef] [Medline]
  4. Thomas KC, Ellis AR, Konrad TR, Holzer CE, Morrissey JP. County-level estimates of mental health professional shortage in the United States. Psychiatr Serv. Oct 2009;60(10):1323-1328. [CrossRef] [Medline]
  5. Patients with depression and anxiety surge as psychologists respond to the coronavirus pandemic. American Psychological Association. URL: [accessed 2024-01-29]
  6. Ussery EN, Fulton JE, Galuska DA, Katzmarzyk PT, Carlson SA. Joint prevalence of sitting time and leisure-time physical activity among US adults, 2015-2016. JAMA. Nov 20, 2018;320(19):2036-2038. [FREE Full text] [CrossRef] [Medline]
  7. Meyer J, Herring M, McDowell C, Lansing J, Brower C, Schuch F, et al. Joint prevalence of physical activity and sitting time during COVID-19 among US adults in April 2020. Prev Med Rep. Dec 2020;20:101256. [FREE Full text] [CrossRef] [Medline]
  8. Ku PW, Steptoe A, Liao Y, Hsueh M, Chen LJ. A cut-off of daily sedentary time and all-cause mortality in adults: a meta-regression analysis involving more than 1 million participants. BMC Med. May 25, 2018;16(1):74. [FREE Full text] [CrossRef] [Medline]
  9. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. SBRN Terminology Consensus Project Participants. Sedentary behavior research network (SBRN) - terminology consensus project process and outcome. Int J Behav Nutr Phys Act. Jun 10, 2017;14(1):75. [FREE Full text] [CrossRef] [Medline]
  10. Jiang L, Cao Y, Ni S, Chen X, Shen M, Lv H, et al. Association of sedentary behavior with anxiety, depression, and suicide ideation in college students. Front Psychiatry. Dec 11, 2020;11:566098. [FREE Full text] [CrossRef] [Medline]
  11. Allen MS, Walter EE, Swann C. Sedentary behaviour and risk of anxiety: a systematic review and meta-analysis. J Affect Disord. Jan 01, 2019;242:5-13. [CrossRef] [Medline]
  12. Teychenne M, Costigan SA, Parker K. The association between sedentary behaviour and risk of anxiety: a systematic review. BMC Public Health. Jun 19, 2015;15(1):513. [FREE Full text] [CrossRef] [Medline]
  13. Teychenne M, Ball K, Salmon J. Sedentary behavior and depression among adults: a review. Int J Behav Med. Dec 2010;17(4):246-254. [CrossRef] [Medline]
  14. Zhai L, Zhang Y, Zhang D. Sedentary behaviour and the risk of depression: a meta-analysis. Br J Sports Med. Jun 02, 2015;49(11):705-709. [CrossRef] [Medline]
  15. Stephens T. Physical activity and mental health in the United States and Canada: evidence from four population surveys. Prev Med. Jan 1988;17(1):35-47. [CrossRef] [Medline]
  16. Lee C, Russell A. Effects of physical activity on emotional well-being among older Australian women: cross-sectional and longitudinal analyses. J Psychosom Res. Feb 2003;54(2):155-160. [CrossRef] [Medline]
  17. Penedo FJ, Dahn JR. Exercise and well-being: a review of mental and physical health benefits associated with physical activity. Curr Opin Psychiatry. Mar 2005;18(2):189-193. [CrossRef] [Medline]
  18. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985;100(2):126-131. [FREE Full text] [Medline]
  19. De Moor MH, Beem AL, Stubbe JH, Boomsma DI, De Geus EJ. Regular exercise, anxiety, depression and personality: a population-based study. Prev Med. Apr 2006;42(4):273-279. [CrossRef] [Medline]
  20. Harris AH, Cronkite R, Moos R. Physical activity, exercise coping, and depression in a 10-year cohort study of depressed patients. J Affect Disord. Jul 2006;93(1-3):79-85. [CrossRef] [Medline]
  21. Garcia D, Archer T, Moradi S, Andersson-Arntén A. Exercise frequency, high activation positive affect, and psychological well-being: beyond age, gender, and occupation. Psychol. 2012;03(04):328-336. [CrossRef]
  22. Hassmén P, Koivula N, Uutela A. Physical exercise and psychological well-being: a population study in Finland. Prev Med. Jan 2000;30(1):17-25. [CrossRef] [Medline]
  23. Goodwin RD. Association between physical activity and mental disorders among adults in the United States. Prev Med. Jun 2003;36(6):698-703. [CrossRef]
  24. Paffenbarger Jr RS, Lee IM, Leung R. Physical activity and personal characteristics associated with depression and suicide in American college men. Acta Psychiatr Scand Suppl. Feb 1994;377(s377):16-22. [CrossRef] [Medline]
  25. Ströhle A, Höfler M, Pfister H, Müller A, Hoyer J, Wittchen H, et al. Physical activity and prevalence and incidence of mental disorders in adolescents and young adults. Psychol Med. Jun 20, 2007;37(11):1657-1666. [CrossRef]
  26. Hyde AL, Conroy DE, Pincus AE, Ram N. Unpacking the feel-good effect of free-time physical activity: between- and within-person associations with pleasant-activated feeling states. J Sport Exerc Psychol. Dec 2011;33(6):884-902. [FREE Full text] [CrossRef] [Medline]
  27. Giacobbi PR, Hausenblas HA, Frye N. A naturalistic assessment of the relationship between personality, daily life events, leisure-time exercise, and mood. Psychology of Sport and Exercise. Jan 2005;6(1):67-81. [CrossRef]
  28. Kanning M, Schlicht W. Be active and become happy: an ecological momentary assessment of physical activity and mood. J Sport Exerc Psychol. Apr 2010;32(2):253-261. [CrossRef] [Medline]
  29. Gianfredi V, Blandi L, Cacitti S, Minelli M, Signorelli C, Amerio A, et al. Depression and objectively measured physical activity: a systematic review and meta-analysis. Int J Environ Res Public Health. May 25, 2020;17(10):3738. [FREE Full text] [CrossRef] [Medline]
  30. McDowell CP, Dishman RK, Gordon BR, Herring MP. Physical activity and anxiety: a systematic review and meta-analysis of prospective cohort studies. Am J Prev Med. Oct 2019;57(4):545-556. [CrossRef] [Medline]
  31. Pearce M, Garcia L, Abbas A, Strain T, Schuch FB, Golubic R, et al. Association between physical activity and risk of depression: a systematic review and meta-analysis. JAMA Psychiatry. Jun 01, 2022;79(6):550-559. [FREE Full text] [CrossRef] [Medline]
  32. Lee IM, Shiroma EJ, Kamada M, Bassett DR, Matthews CE, Buring JE. Association of step volume and intensity with all-cause mortality in older women. JAMA Intern Med. Aug 01, 2019;179(8):1105-1112. [FREE Full text] [CrossRef] [Medline]
  33. Harvey SB, Øverland S, Hatch SL, Wessely S, Mykletun A, Hotopf M. Exercise and the prevention of depression: results of the HUNT cohort study. Am J Psychiatry. Jan 01, 2018;175(1):28-36. [CrossRef] [Medline]
  34. Stathopoulou G, Powers MB, Berry AC, Smits JA, Otto MW. Exercise interventions for mental health: a quantitative and qualitative review. Clin Psychol Sci Pract. 2006;13(2):179-193. [CrossRef]
  35. Otto MW, Church TS, Craft LL, Greer TL, Smits JA, Trivedi MH. Exercise for mood and anxiety disorders. Prim Care Companion J Clin Psychiatry. Aug 15, 2007;9(4):287-294. [FREE Full text] [CrossRef] [Medline]
  36. DiLorenzo TM, Bargman EP, Stucky-Ropp R, Brassington GS, Frensch PA, LaFontaine T. Long-term effects of aerobic exercise on psychological outcomes. Prev Med. Jan 1999;28(1):75-85. [CrossRef] [Medline]
  37. Kishida M, Elavsky S. Daily physical activity enhances resilient resources for symptom management in middle-aged women. Health Psychol. Jul 2015;34(7):756-764. [CrossRef] [Medline]
  38. Flueckiger L, Lieb R, Meyer AH, Witthauer C, Mata J. The importance of physical activity and sleep for affect on stressful days: two intensive longitudinal studies. Emotion. Jun 2016;16(4):488-497. [CrossRef] [Medline]
  39. Forcier K, Stroud LR, Papandonatos GD, Hitsman B, Reiches M, Krishnamoorthy J, et al. Links between physical fitness and cardiovascular reactivity and recovery to psychological stressors: a meta-analysis. Health Psychol. Nov 2006;25(6):723-739. [CrossRef] [Medline]
  40. Bernstein EE, Curtiss JE, Wu GW, Barreira PJ, McNally RJ. Exercise and emotion dynamics: an experience sampling study. Emotion. Jun 2019;19(4):637-644. [CrossRef] [Medline]
  41. Blumenthal JA, Emery CF, Walsh MA, Cox DR, Kuhn CM, Williams RB, et al. Exercise training in healthy type A middle-aged men: effects on behavioral and cardiovascular responses. Psychosom Med. 1988;50(4):418-433. [CrossRef] [Medline]
  42. Jackson EM, Dishman RK. Cardiorespiratory fitness and laboratory stress: a meta-regression analysis. Psychophysiology. Jan 2006;43(1):57-72. [CrossRef] [Medline]
  43. Rejeski WJ, Thompson A, Brubaker PH, Miller HS. Acute exercise: buffering psychosocial stress responses in women. Health Psychol. 1992;11(6):355-362. [CrossRef] [Medline]
  44. Szuhany KL, Bugatti M, Otto MW. A meta-analytic review of the effects of exercise on brain-derived neurotrophic factor. J Psychiatr Res. Jan 2015;60:56-64. [FREE Full text] [CrossRef] [Medline]
  45. Bernstein EE, McNally RJ. Acute aerobic exercise helps overcome emotion regulation deficits. Cogn Emot. Jun 04, 2017;31(4):834-843. [CrossRef] [Medline]
  46. Bernstein EE, McNally RJ. Acute aerobic exercise hastens emotional recovery from a subsequent stressor. Health Psychol. Jun 2017;36(6):560-567. [CrossRef] [Medline]
  47. Kline CE. The bidirectional relationship between exercise and sleep: implications for exercise adherence and sleep improvement. Am J Lifestyle Med. Aug 07, 2014;8(6):375-379. [FREE Full text] [CrossRef] [Medline]
  48. Scott AJ, Webb TL, Martyn-St James M, Rowse G, Weich S. Improving sleep quality leads to better mental health: a meta-analysis of randomised controlled trials. Sleep Med Rev. Dec 2021;60:101556. [FREE Full text] [CrossRef] [Medline]
  49. Carraça EV, Encantado J, Battista F, Beaulieu K, Blundell JE, Busetto L, et al. Effect of exercise training on psychological outcomes in adults with overweight or obesity: a systematic review and meta-analysis. Obes Rev. Jul 06, 2021;22 Suppl 4(Suppl 4):e13261. [FREE Full text] [CrossRef] [Medline]
  50. Frank E, Wallace ML, Hall M, Hasler B, Levenson JC, Janney CA, et al. An Integrated risk reduction intervention can reduce body mass index in individuals being treated for bipolar I disorder: results from a randomized trial. Bipolar Disord. Jun 12, 2015;17(4):424-437. [FREE Full text] [CrossRef] [Medline]
  51. van Zanten JJ, Fenton SA, Brady S, Metsios GS, Duda JL, Kitas GD. Mental health and psychological wellbeing in rheumatoid arthritis during COVID-19 - can physical activity help? Mediterr J Rheumatol. Sep 2020;31(Suppl 2):284-287. [FREE Full text] [CrossRef] [Medline]
  52. Stubbs B, Vancampfort D, Rosenbaum S, Firth J, Cosco T, Veronese N, et al. An examination of the anxiolytic effects of exercise for people with anxiety and stress-related disorders: a meta-analysis. Psychiatry Res. Mar 2017;249:102-108. [CrossRef] [Medline]
  53. Petruzzello SJ, Landers DM, Hatfield BD, Kubitz KA, Salazar W. A meta-analysis on the anxiety-reducing effects of acute and chronic exercise. Outcomes and mechanisms. Sports Med. Mar 1991;11(3):143-182. [CrossRef] [Medline]
  54. Asmundson GJ, Fetzner MG, Deboer LB, Powers MB, Otto MW, Smits JA. Let's get physical: a contemporary review of the anxiolytic effects of exercise for anxiety and its disorders. Depress Anxiety. Apr 08, 2013;30(4):362-373. [CrossRef] [Medline]
  55. Wipfli BM, Rethorst CD, Landers DM. The anxiolytic effects of exercise: a meta-analysis of randomized trials and dose-response analysis. J Sport Exerc Psychol. Aug 2008;30(4):392-410. [CrossRef] [Medline]
  56. Bailey AP, Hetrick SE, Rosenbaum S, Purcell R, Parker AG. Treating depression with physical activity in adolescents and young adults: a systematic review and meta-analysis of randomised controlled trials. Psychol Med. Oct 10, 2017;48(7):1068-1083. [CrossRef]
  57. Schuch FB, Vancampfort D, Richards J, Rosenbaum S, Ward PB, Stubbs B. Exercise as a treatment for depression: a meta-analysis adjusting for publication bias. J Psychiatr Res. Jun 2016;77:42-51. [FREE Full text] [CrossRef] [Medline]
  58. Blumenthal JA, Babyak MA, Moore KA, Craighead WE, Herman S, Khatri P, et al. Effects of exercise training on older patients with major depression. Arch Intern Med. Oct 25, 1999;159(19):2349-2356. [CrossRef] [Medline]
  59. Fetzner MG, Asmundson GJ. Aerobic exercise reduces symptoms of posttraumatic stress disorder: a randomized controlled trial. Cogn Behav Ther. 2015;44(4):301-313. [CrossRef] [Medline]
  60. Abrantes AM, Farris SG, Brown RA, Greenberg BD, Strong DR, McLaughlin NC, et al. Acute effects of aerobic exercise on negative affect and obsessions and compulsions in individuals with obsessive-compulsive disorder. J Affect Disord. Feb 15, 2019;245:991-997. [FREE Full text] [CrossRef] [Medline]
  61. Brown RA, Abrantes AM, Strong DR, Mancebo MC, Menard J, Rasmussen SA, et al. A pilot study of moderate-intensity aerobic exercise for obsessive compulsive disorder. J Nerv Ment Dis. Jun 2007;195(6):514-520. [CrossRef] [Medline]
  62. Rosenbaum S, Tiedemann A, Sherrington C, Curtis J, Ward PB. Physical activity interventions for people with mental illness: a systematic review and meta-analysis. J Clin Psychiatry. Sep 2014;75(9):964-974. [CrossRef] [Medline]
  63. Janney CA, Bauer MS, Kilbourne AM. Self-management and bipolar disorder--a clinician's guide to the literature 2011-2014. Curr Psychiatry Rep. Sep 16, 2014;16(9):485. [CrossRef] [Medline]
  64. Rector NA, Richter MA, Lerman B, Regev R. A pilot test of the additive benefits of physical exercise to CBT for OCD. Cogn Behav Ther. Mar 04, 2015;44(4):328-340. [CrossRef] [Medline]
  65. Powers MB, Medina JL, Burns S, Kauffman BY, Monfils M, Asmundson GJ, et al. Exercise augmentation of exposure therapy for PTSD: rationale and pilot efficacy data. Cogn Behav Ther. 2015;44(4):314-327. [FREE Full text] [CrossRef] [Medline]
  66. Bourbeau K, Moriarty T, Ayanniyi A, Zuhl M. The combined effect of exercise and behavioral therapy for depression and anxiety: systematic review and meta-analysis. Behav Sci (Basel). Jul 14, 2020;10(7):116. [FREE Full text] [CrossRef] [Medline]
  67. Frederiksen KP, Stavestrand SH, Venemyr SK, Sirevåg K, Hovland A. Physical exercise as an add-on treatment to cognitive behavioural therapy for anxiety: a systematic review. Behav Cogn Psychother. Sep 2021;49(5):626-640. [CrossRef] [Medline]
  68. Whitfield GP, Hyde ET, Carlson SA. Participation in leisure-time aerobic physical activity among adults, national health interview survey, 1998-2018. J Phys Act Health. Aug 01, 2021;18(S1):S25-S36. [FREE Full text] [CrossRef] [Medline]
  69. McDowell CP, Dishman RK, Vancampfort D, Hallgren M, Stubbs B, MacDonncha C, et al. Physical activity and generalized anxiety disorder: results from The Irish Longitudinal Study on Ageing (TILDA). Int J Epidemiol. Oct 01, 2018;47(5):1443-1453. [CrossRef] [Medline]
  70. Janney CA, Brzoznowski KF, Richardson CR, Dopp RR, Segar ML, Ganoczy D, et al. Moving towards wellness: physical activity practices, perspectives, and preferences of users of outpatient mental health service. Gen Hosp Psychiatry. Nov 2017;49:63-66. [CrossRef] [Medline]
  71. Vancampfort D, Stubbs B, Herring MP, Hallgren M, Koyanagi A. Sedentary behavior and anxiety: association and influential factors among 42,469 community-dwelling adults in six low- and middle-income countries. General Hospital Psychiatry. Jan 2018;50:26-32. [CrossRef] [Medline]
  72. Stubbs B, Vancampfort D, Firth J, Schuch FB, Hallgren M, Smith L, et al. Relationship between sedentary behavior and depression: a mediation analysis of influential factors across the lifespan among 42,469 people in low- and middle-income countries. J Affect Disord. Mar 15, 2018;229:231-238. [FREE Full text] [CrossRef] [Medline]
  73. Seime RJ, Vickers KS. The challenges of treating depression with exercise: from evidence to practice. Clin Psychol Sci Pract. Nov 2001;13:194-197. [CrossRef] [Medline]
  74. Stanton R, Franck C, Reaburn P, Happell B. A pilot study of the views of general practitioners regarding exercise for the treatment of depression. Perspect Psychiatr Care. Oct 13, 2015;51(4):253-259. [CrossRef] [Medline]
  75. Pollock KM. Exercise in treating depression: broadening the psychotherapist's role. J Clin Psychol. Nov 2001;57(11):1289-1300. [CrossRef] [Medline]
  76. Andrade LH, Alonso J, Mneimneh Z, Wells JE, Al-Hamzawi A, Borges G, et al. Barriers to mental health treatment: results from the WHO World Mental Health surveys. Psychol Med. Aug 09, 2013;44(6):1303-1317. [CrossRef]
  77. Boisseau CL, Schwartzman CM, Lawton J, Mancebo MC. App-guided exposure and response prevention for obsessive compulsive disorder: an open pilot trial. Cogn Behav Ther. Nov 05, 2017;46(6):447-458. [CrossRef] [Medline]
  78. Lee EB, Hoepfl C, Werner C, McIngvale E. A review of tech-based self-help treatment programs for obsessive-compulsive disorder. J Obsessive Compuls Relat Disord. Oct 2019;23:100473. [CrossRef]
  79. Ramos G, Chavira DA. Use of technology to provide mental health care for racial and ethnic minorities: evidence, promise, and challenges. Cogn Behav Pract. Feb 2022;29(1):15-40. [CrossRef]
  80. Manini TM, Carr LJ, King AC, Marshall S, Robinson T, Rejeski W. Interventions to reduce sedentary behavior. Med Sci Sports Exerc. Jun 2015;47(6):1306-1310. [FREE Full text] [CrossRef] [Medline]
  81. Ward DS, Evenson KR, Vaughn A, Rodgers AB, Troiano RP. Accelerometer use in physical activity: best practices and research recommendations. Med Sci Sports Exerc. Nov 2005;37(11 Suppl):S582-S588. [CrossRef] [Medline]
  82. Boudet G, Chausse P, Thivel D, Rousset S, Mermillod M, Baker JS, et al. How to measure sedentary behavior at work? Front Public Health. Jul 5, 2019;7:167. [FREE Full text] [CrossRef] [Medline]
  83. Germini F, Noronha N, Borg Debono V, Abraham Philip B, Pete D, Navarro T, et al. Accuracy and acceptability of wrist-wearable activity-tracking devices: systematic review of the literature. J Med Internet Res. Jan 21, 2022;24(1):e30791. [FREE Full text] [CrossRef] [Medline]
  84. Ueno DT, Guerra PH, Christofoletti AE, Bonolo A, Nakamura PM, Kokubun E. Mobile health apps to reduce sedentary behavior: a scoping review. Health Promot Int. Apr 29, 2022;37(2):daab124. [CrossRef] [Medline]
  85. Stephenson A, McDonough SM, Murphy MH, Nugent CD, Mair JL. Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. Aug 11, 2017;14(1):105. [FREE Full text] [CrossRef] [Medline]
  86. Kim HN, Seo K. Smartphone-based health program for improving physical activity and tackling obesity for young adults: a systematic review and meta-analysis. Int J Environ Res Public Health. Dec 18, 2019;17(1):15. [FREE Full text] [CrossRef] [Medline]
  87. He Z, Wu H, Yu F, Fu J, Sun S, Huang T, et al. Effects of smartphone-based interventions on physical activity in children and adolescents: systematic review and meta-analysis. JMIR Mhealth Uhealth. Feb 01, 2021;9(2):e22601. [FREE Full text] [CrossRef] [Medline]
  88. Han M, Lee E. Effectiveness of mobile health application use to improve health behavior changes: a systematic review of randomized controlled trials. Healthc Inform Res. Jul 2018;24(3):207-226. [FREE Full text] [CrossRef] [Medline]
  89. Litman L, Rosen Z, Spierer D, Weinberger-Litman S, Goldschein A, Robinson J. Mobile exercise apps and increased leisure time exercise activity: a moderated mediation analysis of the role of self-efficacy and barriers. J Med Internet Res. Aug 14, 2015;17(8):e195. [FREE Full text] [CrossRef] [Medline]
  90. Perrin A. Mobile technology and home broadband 2021. Pew Research Center. 2021. URL: [accessed 2024-02-20]
  91. Compernolle S, DeSmet A, Poppe L, Crombez G, De Bourdeaudhuij I, Cardon G, et al. Effectiveness of interventions using self-monitoring to reduce sedentary behavior in adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. Aug 13, 2019;16(1):63. [FREE Full text] [CrossRef] [Medline]
  92. Young AS, Cohen AN, Niv N, Nowlin-Finch N, Oberman RS, Olmos-Ochoa TT, et al. Mobile phone and smartphone use by people with serious mental illness. Psychiatr Serv. Mar 01, 2020;71(3):280-283. [FREE Full text] [CrossRef] [Medline]
  93. Ho'gelen EI, Akdede BB, Alptekin K. M101. prevelance use of technological devices and internet among patients diagnosed with schizophrenia and schizoaffective disorder. Schizophr Bull. 2020;46(Suppl 1):S173. [CrossRef]
  94. Torous J, Wisniewski H, Liu G, Keshavan M. Mental health mobile phone app usage, concerns, and benefits among psychiatric outpatients: comparative survey study. JMIR Ment Health. Nov 16, 2018;5(4):e11715. [FREE Full text] [CrossRef] [Medline]
  95. Schembre SM, Liao Y, Robertson MC, Dunton GF, Kerr J, Haffey ME, et al. Just-in-time feedback in diet and physical activity interventions: systematic review and practical design framework. J Med Internet Res. Mar 22, 2018;20(3):e106. [FREE Full text] [CrossRef] [Medline]
  96. Smyth JM, Sliwinski MJ, Zawadzki MJ, Scott SB, Conroy DE, Lanza ST, et al. Everyday stress response targets in the science of behavior change. Behav Res Ther. Feb 2018;101:20-29. [FREE Full text] [CrossRef] [Medline]
  97. Mazeas A, Duclos M, Pereira B, Chalabaev A. Evaluating the effectiveness of gamification on physical activity: systematic review and meta-analysis of randomized controlled trials. J Med Internet Res. Jan 04, 2022;24(1):e26779. [FREE Full text] [CrossRef] [Medline]
  98. Cotton V, Patel MS. Gamification use and design in popular health and fitness mobile applications. Am J Health Promot. Mar 2019;33(3):448-451. [FREE Full text] [CrossRef] [Medline]
  99. Aguilera A, Figueroa CA, Hernandez-Ramos R, Sarkar U, Cemballi A, Gomez-Pathak L, et al. mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study. BMJ Open. Aug 20, 2020;10(8):e034723. [FREE Full text] [CrossRef] [Medline]
  100. Broers ER, Gavidia G, Wetzels M, Ribas V, Ayoola I, Piera-Jimenez J, et al. Usefulness of a lifestyle intervention in patients with cardiovascular disease. Am J Cardiol. Feb 01, 2020;125(3):370-375. [FREE Full text] [CrossRef] [Medline]
  101. Damschroder LJ, Buis LR, McCant FA, Kim HM, Evans R, Oddone EZ, et al. Effect of adding telephone-based brief coaching to an mHealth app (stay strong) for promoting physical activity among veterans: randomized controlled trial. J Med Internet Res. Aug 04, 2020;22(8):e19216. [FREE Full text] [CrossRef] [Medline]
  102. Edney SM, Olds TS, Ryan JC, Vandelanotte C, Plotnikoff RC, Curtis RG, et al. A social networking and gamified app to increase physical activity: cluster RCT. Am J Prev Med. Feb 2020;58(2):e51-e62. [CrossRef] [Medline]
  103. García-Estela A, Angarita-Osorio N, Alonso S, Polo M, Roldán-Berengué M, Messaggi-Sartor M, et al. Improving depressive symptoms through personalised exercise and activation (IDEA): study protocol for a randomised controlled trial. Int J Environ Res Public Health. Jun 10, 2021;18(12):6306. [FREE Full text] [CrossRef] [Medline]
  104. Guo Y, Hong YA, Cai W, Li L, Hao Y, Qiao J, et al. Effect of a WeChat-based intervention (Run4Love) on depressive symptoms among people living with HIV in China: a randomized controlled trial. J Med Internet Res. Feb 09, 2020;22(2):e16715. [FREE Full text] [CrossRef] [Medline]
  105. Haufe S, Kahl KG, Kerling A, Protte G, Bayerle P, Stenner HT, et al. Employers with metabolic syndrome and increased depression/anxiety severity profit most from structured exercise intervention for work ability and quality of life. Front Psychiatry. Jun 18, 2020;11:562. [FREE Full text] [CrossRef] [Medline]
  106. Kim A, Yun SJ, Sung KS, Kim Y, Jo JY, Cho H, et al. Exercise management using a mobile app in patients with parkinsonism: prospective, open-label, single-arm pilot study. JMIR Mhealth Uhealth. Aug 31, 2021;9(8):e27662. [FREE Full text] [CrossRef] [Medline]
  107. Lin J, Wurst R, Paganini S, Hohberg V, Kinkel S, Göhner W, et al. A group- and smartphone-based psychological intervention to increase and maintain physical activity in patients with musculoskeletal conditions: study protocol for a randomized controlled trial ("MoVo-App"). Trials. Jun 08, 2020;21(1):502. [FREE Full text] [CrossRef] [Medline]
  108. Ma J, Yank V, Lv N, Goldhaber-Fiebert JD, Lewis MA, Kramer MK, et al. Research aimed at improving both mood and weight (RAINBOW) in primary care: a type 1 hybrid design randomized controlled trial. Contemp Clin Trials. Jul 2015;43:260-278. [FREE Full text] [CrossRef] [Medline]
  109. Nadal C, Earley C, Enrique A, Vigano N, Sas C, Richards D, et al. Integration of a smartwatch within an internet-delivered intervention for depression: protocol for a feasibility randomized controlled trial on acceptance. Contemp Clin Trials. Apr 2021;103:106323. [FREE Full text] [CrossRef] [Medline]
  110. Park LG, Elnaggar A, Lee SJ, Merek S, Hoffmann TJ, Von Oppenfeld J, et al. Mobile health intervention promoting physical activity in adults post cardiac rehabilitation: pilot randomized controlled trial. JMIR Form Res. Apr 16, 2021;5(4):e20468. [FREE Full text] [CrossRef] [Medline]
  111. Puszkiewicz P, Roberts AL, Smith L, Wardle J, Fisher A. Assessment of cancer survivors' experiences of using a publicly available physical activity mobile application. JMIR Cancer. May 31, 2016;2(1):e7. [FREE Full text] [CrossRef] [Medline]
  112. Puterman E, Hives B, Mazara N, Grishin N, Webster J, Hutton S, et al. COVID-19 Pandemic and Exercise (COPE) trial: a multigroup pragmatic randomised controlled trial examining effects of app-based at-home exercise programs on depressive symptoms. Br J Sports Med. May 2022;56(10):546-552. [FREE Full text] [CrossRef] [Medline]
  113. Skrepnik N, Spitzer A, Altman R, Hoekstra J, Stewart J, Toselli R. Assessing the impact of a novel smartphone application compared with standard follow-up on mobility of patients with knee osteoarthritis following treatment with Hylan G-F 20: a randomized controlled trial. JMIR Mhealth Uhealth. May 09, 2017;5(5):e64. [FREE Full text] [CrossRef] [Medline]
  114. Stephens S, Schneiderman JE, Finlayson M, Berenbaum T, Motl RW, Yeh EA. Feasibility of a theory-informed mobile app for changing physical activity in youth with multiple sclerosis. Mult Scler Relat Disord. Feb 2022;58:103467. [CrossRef] [Medline]
  115. Teychenne M, Abbott G, Stephens LD, Opie RS, Olander EK, Brennan L, et al. Mums on the move: a pilot randomised controlled trial of a home-based physical activity intervention for mothers at risk of postnatal depression. Midwifery. Feb 2021;93:102898. [CrossRef] [Medline]
  116. Wilczynska M, Lubans DR, Plotnikoff RC. The effects of the eCoFit RCT on depression and anxiety symptoms among adults with or at risk of type 2 diabetes. Psychol Health Med. Aug 16, 2022;27(7):1421-1430. [CrossRef] [Medline]
  117. Wong VW, Ho FY, Shi NK, Tong JT, Chung KF, Yeung WF, et al. Smartphone-delivered multicomponent lifestyle medicine intervention for depressive symptoms: a randomized controlled trial. J Consult Clin Psychol. Dec 2021;89(12):970-984. [CrossRef] [Medline]
  118. Roberts AL, Fisher A, Smith L, Heinrich M, Potts HW. Digital health behaviour change interventions targeting physical activity and diet in cancer survivors: a systematic review and meta-analysis. J Cancer Surviv. Dec 2017;11(6):704-719. [FREE Full text] [CrossRef] [Medline]
  119. Marques A, Santos T, Martins J, Matos MG, Valeiro MG. The association between physical activity and chronic diseases in European adults. Eur J Sport Sci. Feb 14, 2018;18(1):140-149. [FREE Full text] [CrossRef] [Medline]
  120. Arietaleanizbeaskoa MS, Sancho A, Olazabal I, Moreno C, Gil E, Garcia-Alvarez A, et al. EfiKroniK group. Effectiveness of physical exercise for people with chronic diseases: the EFIKRONIK study protocol for a hybrid, clinical and implementation randomized trial. BMC Fam Pract. Nov 06, 2020;21(1):227. [FREE Full text] [CrossRef] [Medline]
  121. Pedersen BK, Saltin B. Exercise as medicine - evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports. Dec 25, 2015;25 Suppl 3:1-72. [CrossRef] [Medline]
  122. Lascar N, Kennedy A, Hancock B, Jenkins D, Andrews RC, Greenfield S, et al. Attitudes and barriers to exercise in adults with type 1 diabetes (T1DM) and how best to address them: a qualitative study. PLoS One. 2014;9(9):e108019. [FREE Full text] [CrossRef] [Medline]
  123. Anderson E, Durstine JL. Physical activity, exercise, and chronic diseases: a brief review. Sports Med Health Sci. Dec 2019;1(1):3-10. [FREE Full text] [CrossRef] [Medline]
  124. Fleming T, Bavin L, Lucassen M, Stasiak K, Hopkins S, Merry S. Beyond the trial: systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety. J Med Internet Res. Jun 06, 2018;20(6):e199. [FREE Full text] [CrossRef] [Medline]
  125. Yang CH, Maher JP, Conroy DE. Implementation of behavior change techniques in mobile applications for physical activity. Am J Prev Med. Apr 2015;48(4):452-455. [CrossRef] [Medline]
  126. Carter DD, Robinson K, Forbes J, Hayes S. Experiences of mobile health in promoting physical activity: a qualitative systematic review and meta-ethnography. PLoS One. 2018;13(12):e0208759. [FREE Full text] [CrossRef] [Medline]
  127. Thraen-Borowski KM, Ellingson LD, Meyer JD, Cadmus-Bertram L. Nonworksite interventions to reduce sedentary behavior among adults: a systematic review. Transl J Am Coll Sports Med. Jun 15, 2017;2(12):68-78. [FREE Full text] [CrossRef] [Medline]
  128. Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. Jun 1983;51(3):390-395. [CrossRef] [Medline]
  129. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991.:179-211. [FREE Full text] [CrossRef]
  130. Deci EL, Ryan EM. Intrinsic Motivation and Self-Determination in Human Behavior. New York, NY. Plenum Press; 1985.
  131. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ. Prentice-Hall, Inc; 1986.
  132. Chase JD, Otmanowski J, Rowland S, Cooper PS. A systematic review and meta-analysis of interventions to reduce sedentary behavior among older adults. Transl Behav Med. Oct 12, 2020;10(5):1078-1085. [CrossRef] [Medline]
  133. Ludwig K, Arthur R, Sculthorpe N, Fountain H, Buchan DS. Text messaging interventions for improvement in physical activity and sedentary behavior in youth: systematic review. JMIR Mhealth Uhealth. Sep 17, 2018;6(9):e10799. [FREE Full text] [CrossRef] [Medline]
  134. Stockwell S, Schofield P, Fisher A, Firth J, Jackson SE, Stubbs B, et al. Digital behavior change interventions to promote physical activity and/or reduce sedentary behavior in older adults: a systematic review and meta-analysis. Exp Gerontol. Jun 2019;120:68-87. [CrossRef] [Medline]
  135. McNally RF, Reese HE. Information-processing approaches to understanding anxiety disorders. In: Antony MM, Stein MB, editors. Oxford Handbook of Anxiety and Related Disorders. Oxford, UK. Oxford University Press; 2009;136-152.
  136. Pan Z, Park C, Brietzke E, Zuckerman H, Rong C, Mansur RB, et al. Cognitive impairment in major depressive disorder. CNS Spectr. Feb 2019;24(1):22-29. [CrossRef] [Medline]
  137. Berggren N, Derakshan N. Attentional control deficits in trait anxiety: why you see them and why you don't. Biol Psychol. Mar 2013;92(3):440-446. [CrossRef] [Medline]
  138. McIntyre RS, Cha DS, Soczynska JK, Woldeyohannes HO, Gallaugher LA, Kudlow P, et al. Cognitive deficits and functional outcomes in major depressive disorder: determinants, substrates, and treatment interventions. Depress Anxiety. Jun 06, 2013;30(6):515-527. [CrossRef] [Medline]
  139. Looyestyn J, Kernot J, Boshoff K, Ryan J, Edney S, Maher C. Does gamification increase engagement with online programs? A systematic review. PLoS One. 2017;12(3):e0173403. [FREE Full text] [CrossRef] [Medline]
  140. Yang Y, Koenigstorfer J. Determinants of fitness app usage and moderating impacts of education-, motivation-, and gamification-related app features on physical activity intentions: cross-sectional survey study. J Med Internet Res. Jul 13, 2021;23(7):e26063. [FREE Full text] [CrossRef] [Medline]
  141. Admon R, Pizzagalli DA. Dysfunctional reward processing in depression. Curr Opin Psychol. Aug 01, 2015;4:114-118. [FREE Full text] [CrossRef] [Medline]
  142. Smits JA, Tart CD, Presnell K, Rosenfield D, Otto MW. Identifying potential barriers to physical activity adherence: anxiety sensitivity and body mass as predictors of fear during exercise. Cogn Behav Ther. Jan 2010;39(1):28-36. [CrossRef] [Medline]
  143. Mason J, Faller Y, LeBouthillier D, Asmundson G. Exercise anxiety: a qualitative analysis of the barriers, facilitators, and psychological processes underlying exercise participation for people with anxiety-related disorders. Ment Health Phys Act. Mar 2019;16:128-139. [CrossRef]
  144. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. Aug 2013;46(1):81-95. [FREE Full text] [CrossRef] [Medline]
  145. Heath GW, Parra DC, Sarmiento OL, Andersen LB, Owen N, Goenka S, et al. Lancet Physical Activity Series Working Group. Evidence-based intervention in physical activity: lessons from around the world. Lancet. Jul 21, 2012;380(9838):272-281. [FREE Full text] [CrossRef] [Medline]
  146. Lewis BA, Napolitano MA, Buman MP, Williams DM, Nigg CR. Future directions in physical activity intervention research: expanding our focus to sedentary behaviors, technology, and dissemination. J Behav Med. Feb 2017;40(1):112-126. [FREE Full text] [CrossRef] [Medline]
  147. Anderson PJ, Bovard RS, Wang Z, Beebe TJ, Murad MH. A survey of social support for exercise and its relationship to health behaviours and health status among endurance Nordic skiers. BMJ Open. Jun 23, 2016;6(6):e010259. [FREE Full text] [CrossRef] [Medline]
  148. Middelweerd A, Te Velde SJ, Abbott G, Timperio A, Brug J, Ball K. Do intrapersonal factors mediate the association of social support with physical activity in young women living in socioeconomically disadvantaged neighbourhoods? A longitudinal mediation analysis. PLoS One. Mar 16, 2017;12(3):e0173231. [FREE Full text] [CrossRef] [Medline]
  149. Duff OM, Walsh DM, Furlong BA, O'Connor NE, Moran KA, Woods CB. Behavior change techniques in physical activity eHealth interventions for people with cardiovascular disease: systematic review. J Med Internet Res. Aug 02, 2017;19(8):e281. [FREE Full text] [CrossRef] [Medline]
  150. Samdal GB, Eide GE, Barth T, Williams G, Meland E. Effective behaviour change techniques for physical activity and healthy eating in overweight and obese adults; systematic review and meta-regression analyses. Int J Behav Nutr Phys Act. Mar 28, 2017;14(1):42. [FREE Full text] [CrossRef] [Medline]
  151. Bakker D, Kazantzis N, Rickwood D, Rickard N. Mental health smartphone apps: review and evidence-based recommendations for future developments. JMIR Ment Health. Mar 01, 2016;3(1):e7. [FREE Full text] [CrossRef] [Medline]
  152. Guerrero-Jiménez M, Ruiz M, Gutiérrez-Rojas L, Jiménez-Muñoz L, Baca-Garcia E, Porras-Segovia A. Use of new technologies for the promotion of physical activity in patients with mental illness: a systematic review. World J Psychiatry. Apr 19, 2023;13(4):182-190. [FREE Full text] [CrossRef] [Medline]
  153. Horvath P. Treatment expectancy as a function of the amount of information presented in therapeutic rationales. J Clin Psychol. Sep 1990;46(5):636-642. [CrossRef] [Medline]
  154. Schofield CA, Ponzini GT, Becker SJ. Evaluating approaches to marketing cognitive behavioral therapy: does evidence matter to consumers? Cogn Behav Ther. Jul 2020;49(4):257-269. [FREE Full text] [CrossRef] [Medline]
  155. Nyström CD, Sandin S, Henriksson P, Henriksson H, Trolle-Lagerros Y, Larsson C, et al. Mobile-based intervention intended to stop obesity in preschool-aged children: the MINISTOP randomized controlled trial. Am J Clin Nutr. Jun 2017;105(6):1327-1335. [FREE Full text] [CrossRef] [Medline]
  156. Hearon BA, Beard C, Kopeski LM, Smits JA, Otto MW, Björgvinsson T. Attending to timely contingencies: promoting physical activity uptake among adults with serious mental illness with an exercise-for-mood vs. an exercise-for-fitness prescription. Behav Med. Feb 21, 2018;44(2):108-115. [CrossRef] [Medline]
  157. McNally RJ. Anxiety sensitivity and panic disorder. Biol Psychiatry. Nov 15, 2002;52(10):938-946. [CrossRef] [Medline]
  158. Wallman-Jones A, Perakakis P, Tsakiris M, Schmidt M. Physical activity and interoceptive processing: theoretical considerations for future research. Int J Psychophysiol. Aug 2021;166:38-49. [FREE Full text] [CrossRef] [Medline]
  159. van Dis EA, van Veen SC, Hagenaars MA, Batelaan NM, Bockting CL, van den Heuvel RM, et al. Long-term outcomes of cognitive behavioral therapy for anxiety-related disorders: a systematic review and meta-analysis. JAMA Psychiatry. Mar 01, 2020;77(3):265-273. [FREE Full text] [CrossRef] [Medline]
  160. Kabat-Zinn J. Wherever You Go, There You Are: Mindfulness Meditation in Everyday Life. New York, NY. Hyperion; 1994.
  161. Schreiner I, Malcolm JP. The benefits of mindfulness meditation: changes in emotional states of depression, anxiety, and stress. Behav change. Feb 22, 2012;25(3):156-168. [FREE Full text] [CrossRef]
  162. Remmers C, Topolinski S, Koole SL. Why being mindful may have more benefits than you realize: mindfulness improves both explicit and implicit mood regulation. Mindfulness. Apr 5, 2016;7(4):829-837. [FREE Full text] [CrossRef] [Medline]
  163. Ribeiro L, Atchley RM, Oken BS. Adherence to practice of mindfulness in novice meditators: practices chosen, amount of time practiced, and long-term effects following a mindfulness-based intervention. Mindfulness (N Y). Apr 2018;9(2):401-411. [FREE Full text] [CrossRef] [Medline]
  164. Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry. Oct 2017;16(3):287-298. [FREE Full text] [CrossRef] [Medline]
  165. Wilhelm S, Weingarden H, Greenberg JL, McCoy TH, Ladis I, Summers BJ, et al. Development and pilot testing of a cognitive-behavioral therapy digital service for body dysmorphic disorder. Behav Ther. Jan 2020;51(1):15-26. [FREE Full text] [CrossRef] [Medline]
  166. Kambeitz-Ilankovic L, Rzayeva U, Völkel L, Wenzel J, Weiske J, Jessen F, et al. A systematic review of digital and face-to-face cognitive behavioral therapy for depression. NPJ Digit Med. Sep 15, 2022;5(1):144. [FREE Full text] [CrossRef] [Medline]
  167. Merom D, Phongsavan P, Wagner R, Chey T, Marnane C, Steel Z, et al. Promoting walking as an adjunct intervention to group cognitive behavioral therapy for anxiety disorders--a pilot group randomized trial. J Anxiety Disord. Aug 2008;22(6):959-968. [CrossRef] [Medline]
  168. Kaushal N, Rhodes RE. Exercise habit formation in new gym members: a longitudinal study. J Behav Med. Aug 2015;38(4):652-663. [CrossRef] [Medline]
  169. Landais LL, Damman OC, Schoonmade LJ, Timmermans DR, Verhagen EA, Jelsma JG. Choice architecture interventions to change physical activity and sedentary behavior: a systematic review of effects on intention, behavior and health outcomes during and after intervention. Int J Behav Nutr Phys Act. Apr 07, 2020;17(1):47. [FREE Full text] [CrossRef] [Medline]
  170. Vandelanotte C, Spathonis KM, Eakin EG, Owen N. Website-delivered physical activity interventions a review of the literature. Am J Prev Med. Jul 2007;33(1):54-64. [CrossRef] [Medline]
  171. Conn VS. Anxiety outcomes after physical activity interventions: meta-analysis findings. Nurs Res. 2010;59(3):224-231. [FREE Full text] [CrossRef] [Medline]
  172. Benjet C, Zainal NH, Albor Y, Alvis-Barranco L, Carrasco-Tapias N, Contreras-Ibáñez CC, et al. A precision treatment model for internet-delivered cognitive behavioral therapy for anxiety and depression among university students: a secondary analysis of a randomized clinical trial. JAMA Psychiatry. Aug 01, 2023;80(8):768-777. [CrossRef] [Medline]
  173. Bernstein EE, Weingarden H, Wolfe EC, Hall MD, Snorrason I, Wilhelm S. Human support in app-based cognitive behavioral therapies for emotional disorders: scoping review. J Med Internet Res. Apr 08, 2022;24(4):e33307. [FREE Full text] [CrossRef] [Medline]
  174. van Rijen D, Ten Hoor GA. A qualitative analysis of facilitators and barriers to physical activity among patients with moderate mental disorders. Z Gesundh Wiss. Jun 01, 2022.:1-16. [FREE Full text] [CrossRef] [Medline]
  175. Searle A, Calnan M, Lewis G, Campbell J, Taylor A, Turner K. Patients' views of physical activity as treatment for depression: a qualitative study. Br J Gen Pract. Apr 01, 2011;61(585):e149-e156. [CrossRef]
  176. Borghouts J, Eikey E, Mark G, De Leon C, Schueller SM, Schneider M, et al. Barriers to and facilitators of user engagement with digital mental health interventions: systematic review. J Med Internet Res. Mar 24, 2021;23(3):e24387. [FREE Full text] [CrossRef] [Medline]
  177. Mikolasek M, Witt CM, Barth J. Adherence to a mindfulness and relaxation self-care app for cancer patients: mixed-methods feasibility study. JMIR Mhealth Uhealth. Dec 06, 2018;6(12):e11271. [FREE Full text] [CrossRef] [Medline]
  178. Arean PA, Hallgren KA, Jordan JT, Gazzaley A, Atkins DC, Heagerty PJ, et al. The use and effectiveness of mobile apps for depression: results from a fully remote clinical trial. J Med Internet Res. Dec 20, 2016;18(12):e330. [FREE Full text] [CrossRef] [Medline]
  179. Bondaronek P, Alkhaldi G, Slee A, Hamilton FL, Murray E. Quality of publicly available physical activity apps: review and content analysis. JMIR Mhealth Uhealth. Mar 21, 2018;6(3):e53. [FREE Full text] [CrossRef] [Medline]
  180. Wallin EE, Mattsson S, Olsson EM. The preference for internet-based psychological interventions by individuals without past or current use of mental health treatment delivered online: a survey study with mixed-methods analysis. JMIR Ment Health. Jun 14, 2016;3(2):e25. [FREE Full text] [CrossRef] [Medline]
  181. Lepore SJ, Rincon MA, Buzaglo JS, Golant M, Lieberman MA, Bauerle Bass S, et al. Digital literacy linked to engagement and psychological benefits among breast cancer survivors in internet-based peer support groups. Eur J Cancer Care (Engl). Jul 18, 2019;28(4):e13134. [FREE Full text] [CrossRef] [Medline]
  182. Mobile fact sheet. Pew Research Center. URL: [accessed 2022-12-15]
  183. Internet/broadband fact sheet. Pew Research Center. 2021. URL: [accessed 2022-12-15]
  184. Young DR, Stewart KJ. A church-based physical activity intervention for African American women. Fam Community Health. 2006;29(2):103-117. [CrossRef] [Medline]
  185. Jenkins F, Jenkins C, Gregoski MJ, Magwood GS. Interventions promoting physical activity in African American women: an integrative review. J Cardiovasc Nurs. 2017;32(1):22-29. [FREE Full text] [CrossRef] [Medline]
  186. Pekmezi DW, Williams DM, Dunsiger S, Jennings EG, Lewis BA, Jakicic JM, et al. Feasibility of using computer-tailored and internet-based interventions to promote physical activity in underserved populations. Telemed J E Health. May 2010;16(4):498-503. [FREE Full text] [CrossRef] [Medline]
  187. Sayer J, Paniagua D, Ballentine S, Sheehan L, Carson M, Nieweglowski K, et al. Community-Based Participatory Research (CBPR) Team. Perspectives on diet and physical activity among urban African Americans with serious mental illness. Soc Work Health Care. Mar 25, 2019;58(5):509-525. [FREE Full text] [CrossRef] [Medline]
  188. Banks-Wallace J, Conn V. Interventions to promote physical activity among African American women. Public Health Nurs. Sep 2002;19(5):321-335. [CrossRef] [Medline]
  189. Blue shield of California's Wellvolution program now offers headspace app to help members reduce stress and increase resilience. Blue Shield of California. 2018. URL: https:/​/news.​​2021/​10/​26/​blue-shield-of-californias-​wellvolution-program-now-offers-headspace-app-to-help-members-reduce-stress-and-increase-resilience [accessed 2022-12-16]
  190. Ginger for Cigna customers. Ginger. URL: [accessed 2022-12-16]
  191. Self-care apps for your everyday life. Kaiser Foundation Health Plan. URL: [accessed 2022-12-16]

CBT: cognitive behavioral therapy
HIIT: high-intensity interval training

Edited by L Buis; submitted 19.01.23; peer-reviewed by K Szuhany, C Janney , G Jonathan, J Brinsley; comments to author 21.07.23; revised version received 12.09.23; accepted 30.11.23; published 15.03.24.


©Emily E Bernstein, Emma C Wolfe, Brynn M Huguenel, Sabine Wilhelm. Originally published in JMIR mHealth and uHealth (, 15.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.