Background: Depression is a prevalent and debilitating mental disorder and a leading cause of disability worldwide. Physical activity (PA) interventions have been shown to alleviate depressive symptoms. However, not all patients have access to PA programing tailored for depression. Internet-guided self-help (IGSH) interventions may be an effective option for increasing PA among people with depression who cannot or prefer not to access supervised exercise treatment.
Objective: We aimed to evaluate the effectiveness of IGSH interventions in increasing PA and alleviating depressive symptoms in people with depression.
Methods: A systematic literature search was conducted for randomized controlled trials and quasiexperimental studies using 9 electronic databases. The review was registered in PROSPERO (2020 CRD42020221713).
Results: A total of 4 randomized controlled trials (430 participants) met the inclusion criteria. Of these, 3 were web-based and 1 was app-based. Three studies found IGSH interventions to have medium to large effects on decreasing depressive symptoms but not on increasing PA compared with waitlist or usual care. One study showed increased self-reported PA but no significant difference in depressive symptoms in the intervention group compared with the control group. Goal setting was the most common behavior change technique used in the interventions. Dropout rates within the intervention groups were relatively low (0%-19%).
Conclusions: Our findings suggested that IGSH PA interventions are feasible and have the potential to reduce depressive symptoms in people with depression. More well-designed and tailored interventions with different combinations of behavior change techniques, particularly those targeting the emotion domain, are needed to assess the overall effectiveness and feasibility of using IGSH interventions to increase PA among people with depression.
Trial Registration: PROSPERO CRD42020221713; https://tinyurl.com/ysaua5bu
Depression is a chronic mental health condition characterized by sadness; anhedonia; and secondary physical, cognitive, and emotional symptoms. People with depression often experience lower quality of life [- ], increased risk of suicide [ , ], and increased risk of having comorbid chronic diseases (eg, diabetes, asthma, chronic lung disease, and coronary heart disease), which can result in premature death [ , ]. Historically, it was estimated that >300 million people have experienced depression worldwide [ ], and most people have never been diagnosed [ , , ]. Surveillance studies have observed a marked increase in the prevalence and severity of depressive symptoms since the beginning of the COVID-19 pandemic. For instance, Bueno-Notivol et al [ ] observed that the prevalence of self-reported depression worldwide was 7 times higher in 2020 (25%) than in 2017 (3.44%). Similarly, a meta-analysis by Nochaiwong et al [ ] estimated that more than a quarter of people globally (28%) self-reported depressive symptoms during the COVID-19 outbreak in 2020.
Physical activity (PA)—“any bodily movement produced by skeletal muscles that results in energy expenditure ”—has long been recognized as an important health-promoting behavior. In recent years, it has also been shown to be beneficial in the prevention and treatment of depressive disorders [ , ]. Exercise, a subset of PA that is typically planned and structured with the goal of increasing or maintaining fitness [ ], is now recommended as a monotherapy for mild to moderate depression in Canada [ ]. PA presents several advantages over conventional treatments for depression (ie, psychotherapy and antidepressant medication). Among them, PA has minimal negative side effects, is affordable, and is potentially more accessible [ ]. Rebar and Taylor [ ] suggested that PA could be a cost-effective method for treating depression worldwide. As an additional benefit, a large body of literature confirmed the positive side effects of engaging in regular PA, including heightened health-related quality of life (ie, physical and mental well-being) [ ], chronic disease prevention (including obesity, type 2 diabetes, coronary heart diseases, and several cancers), and reduced risk of premature mortality [ , ].
Globally, a large minority (31.1%) of adults do not meet the minimum recommended levels of PA . Data from the 2016 to 2017 Canadian Health Measures Survey suggested that only 16% of Canadian adults meet the current PA recommendation of 150 minutes of moderate to vigorous PA (MVPA) per week [ ]. People with depression are more likely to experience lower PA levels than those without depression [ , ], in part because of symptoms such as pain and discomfort, insomnia, cognitive difficulties, fatigue, and anhedonia [ , , ]. Tailored approaches to help people with depression that initiate and maintain PA are needed [ ].
Internet-Guided Self-help Interventions
Internet-guided self-help (IGSH) interventions, a form of eHealth intervention, could be 1 mechanism for supporting PA behavior changes among people with depression. eHealth interventions are defined as those that use information and communication technologies to enable health care, including supporting health behavior engagement [, ]. As such, eHealth interventions are quite broad and include just-in-time adaptive interventions, wearable technology, telehealth, and social media. Research has demonstrated that most people with mental disorders have an interest in trying eHealth interventions (such as smartphone apps) to monitor and manage their health concerns [ , ].
IGSH interventions are characterized by web-based or app-based programs that are primarily self-guided. Some will offer limited support from a professional or paraprofessional . Compared with synchronous eHealth interventions that likely require costlier direct consultation (eg, telehealth, live internet-based therapy, and live-streamed exercise sessions) [ ], IGSH programs delivered via the web or mobile devices have the potential for broad reach and scalability at a relatively low cost. They also allow participants to access the intervention content at their own pace [ - ]. These features may be particularly beneficial for people with depression who experience symptoms, such as fatigue and disrupted sleep patterns. In addition, many people with depression experience stigma that negatively affects treatment-seeking [ ] and have preferences for managing symptoms on their own [ ]. Offering self-help interventions could be potentially useful in increasing help-seeking rates by mitigating stigma [ ].
Systematic reviews and meta-analyses have found that eHealth interventions are effective in increasing short-term PA participation in nonclinical populations, including young people [, ], adults [ ], and older adults [ , ]. Other research on eHealth interventions suggests that interventions that adopt theory or incorporate evidence-based behavior change techniques (BCTs) are generally associated with greater effects and adherence [ , ]. A systematic review of meta-analyses on the effectiveness of self-help and internet-guided interventions has suggested that these programs are effective in treating depression [ , ]. In fact, computerized cognitive behavioral therapy (CBT) has been recommended as a treatment for subthreshold or mild to moderate depression in the United Kingdom [ ]. Similarly, a systematic review by Andersson and Cuijpers [ ] suggested that guided internet-based psychological interventions were more effective than unguided interventions for depression among adults.
Less is known about whether IGSH PA interventions for people with depression are effective at (1) increasing PA engagement and (2) reducing depressive symptoms. To date, 2 reviews have investigated eHealth PA interventions for individuals with mental illnesses [, ], and 1 review [ ] specifically examined web-based interventions. However, these reviews investigated eHealth interventions for mental illness generally, rather than IGSH programs for depression specifically. These are noteworthy distinctions, as the strongest research evidence of the benefits of PA-based treatments is for depression [ ]; to our knowledge, no clinical guidelines exist for PA-based treatment for anxiety, schizophrenia, or other mental health conditions. In addition, the reviews included both experimental and observational studies [ ]. Given the rapid pace of technological development and growing concerns of physical inactivity among people with depression, it is likely that this research field has expanded in recent years. There is a need for an updated systematic review of high-quality studies specific to depression. Thus, the primary objective of this systematic review was to assess the effectiveness of IGSH interventions in promoting PA and alleviating depressive symptoms in people with depression. The secondary objective of this study was to understand study characteristics, such as attrition rates and intervention design, to explore factors associated with successful interventions and areas for future growth.
This systematic review was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach [, ]. The protocol was registered in PROSPERO (CRD42020221713). The PRISMA checklist is provided in .
A total of 2 rounds of search were conducted in December 2020 and November 2021. The search strategy was developed in consultation with a university rehabilitation sciences librarian. In all, 7 electronic databases were searched: MEDLINE (via Ovid), PsycINFO (EBSCOhost), CINAHL (EBSCOhost), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL; via Ovid), Embase (via Ovid), and SportDiscus (EBSCOhost). In addition, OpenGrey and ProQuest Dissertations were searched to identify gray literature that matched the inclusion criteria. Finally, reference lists of the included studies were searched to identify additional eligible studies.
The December 2020 search used a combination of controlled vocabulary (eg, Medical Subject Headings) and keywords related to “physical activity,” “depression,” and “eHealth.” For comprehensiveness, the search included keywords for all types of eHealth interventions. The search strategies for all databases are presented in. The selected keywords were obtained from previous systematic reviews and protocols in relevant areas.
A revised search was conducted in November 2021, with two changes: (1) the results were limited to the period from December 1, 2020, to November 5, 2021, and (2) the search strategy was updated to better reflect the inclusion criteria and reduce irrelevant records informed by the experience of the December 2020 search. For example, the keywords “text messaging” and “video conferencing” were deleted. The updated search strategies for all included databases are presented in.
The study selection criteria were based on the Population, Intervention, Comparison, Outcome, Study (PICOS) design framework [, ]. Studies that met the following criteria were included in this review.
Individuals with a clinical diagnosis of depression (eg, the Diagnostic and Statistical Manual of Mental Disorders criteria) or individuals with clinically significant depressive symptoms based on a validated self-report tool (eg, Patient Health Questionnaire 9-Item or Beck Depression Inventory-II [BDI-II] scores) were considered eligible for inclusion. Studies that used nonvalidated items (eg, “Are you depressed? Yes/No”) or those who did not use recognized diagnostic criteria were excluded. There were no restrictions based on age, gender, nationality, or ethnicity.
Interventions were considered eligible if they were delivered via web-based platforms or mobile apps (eg, via smartphones and tablets) and included content designed to increase PA (eg, asynchronous PA programing, education, and PA goal setting). Studies were included even if promoting PA was the secondary objective, so long as changes in PA were measured and reported. By definition, interventions were primarily self-guided or automatic; however, interventions that offered degrees of human support (eg, providing personalized feedback via telephone or email) were permitted. In contrast, interventions primarily based on in-person support (eg, telephone counseling and SMS text messaging) were excluded. Similarly, live-streamed interventions (eg, live Zoom yoga classes) were excluded, as these are synchronous events organized in a live internet-based space. There were no restrictions based on the type of PA, PA intensity, bout length, program frequency or duration, or follow-up period.
Any comparators were considered for inclusion. For instance, a PA intervention delivered in person, an alternative eHealth intervention, or a waitlist control group. No restrictions were placed on the nature of the comparison group.
The primary outcome was a change in PA levels. Both device-based (eg, accelerometer and pedometer) and self-report (eg, PA diary and questionnaires) measures were considered. Secondary outcomes included changes in the severity of depression, assessed by validated measures, and the acceptability of treatment assessed by (1) reported indicators of intervention engagement  and (2) dropout rates at postintervention. Engagement was defined as the extent to which the participants undertook the intervention. Dropout rate, also called attrition rate, was defined as the percentage of participants who were randomized to a group but failed to complete it.
Randomized controlled trials (RCTs) and quasiexperimental studies with control groups and pre- and posttest measurements were considered. Studies were eligible if they were a published original manuscript or thesis or dissertation. Papers were excluded if they did not contain original findings (eg, editorials or reviews), if they were research protocols or proposals, or if they were conference abstracts.
Papers were considered eligible for inclusion if they were published in English. There were no limitations on the date of publication.
A total of 2 authors (first round: YT and JL; second round: YT and MG) independently screened the articles for inclusion using the agreed-upon study eligibility or ineligibility criteria. Screening occurred in 3 phases, facilitated by the Covidence systematic review software (Veritas Health Innovation). First, duplicate records were removed automatically using Covidence. Second, titles and abstracts were independently screened for relevance by 2 reviewers. In case of disagreement, the reviewers met to discuss and reach a consensus. Finally, full-text copies of the selected studies were screened (YT, JL, DD, and MG) to confirm their eligibility. Disagreements between the 2 researchers were resolved through discussion. A third reviewer (GF) was consulted if consensus could not be reached. Following selection, the reference list for each included study was searched to identify additional eligible studies. The PRISMA flow diagrams are presented inand .
The following items were extracted by 1 author (YT) and reviewed for accuracy by another reviewer (first round: DD and second round: MG): (1) author and year; (2) study design; (3) country; (4) sample size; (5) participant characteristics, such as mean age and gender; (6) inclusion and exclusion criteria; (7) intervention details, including the level of contact; (8) comparator; (9) main outcomes (measurement tools); (10) additional outcomes (measurement tools); (11) main findings with regard to PA and depression symptomatology; (12) follow-up period; (13) BCTs; and (14) funding sources. A third author (GF) was consulted to resolve discrepancies.
Level of contact was categorized as high, moderate, and low, following procedures described by Ma et al . If the support was directly provided by a qualified therapist (eg, exercise trainers providing personalized feedback), the level of contact was high. If the support was provided by members of the research team or trained students, it was considered to be moderate. If there was no direct contact with an interventionist (eg, only automated reminder emails were sent out), the contact was considered to be low.
Risk of Bias
The Cochrane risk of bias tool (ROB 2; version 2) [, ] was used to assess the risk of bias for both PA and depression outcomes. This tool includes 5 domains of risk of bias: randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. The risk of bias for each outcome in each study was judged as high, some concerns, or low. Risk of bias was assessed by 2 independent reviewers (YT and MG). In case of disagreement, the reviewers met to discuss and reach a consensus. The remaining disagreements were resolved by consulting a third author (GF).
Meta-analysis was not feasible because the included studies were too heterogeneous in their design and reporting of results. The study interventions and outcome characteristics were summarized using narrative synthesis and descriptive statistics. When the effect size was reported as Cohen d (standardized mean difference), it was interpreted as small (Cohen d=0.2), moderate (Cohen d=0.5), or large (Cohen d=0.8) .
Confidence in the Cumulative Evidence
The Grading of Recommendation, Assessment, Development and Evaluation (GRADE)  criteria were used to rate the certainty of the cumulative evidence based on the risk of bias, imprecision, inconsistency, indirectness, and publication bias. The quality of evidence was categorized as high, moderate, low, or very low.
A total of 45,461 records were identified: 42,052 in December 2020 and an additional 3409 in November 2021. After removing duplicates, 28,668 articles underwent title or abstract screening and 95 underwent full-text review. The first round of searches (December 2020) produced 4 articles. The second round of searches (November 2021) produced 1 additional article, which was subsequently deleted because it was a duplicate. A total of 4 eligible studies were included in this systematic review. The PRISMA flow diagrams are presented inand .
Characteristics of Included Trials
and summarize the characteristics of the 4 included studies. All 4 included studies were dual-arm RCTs. None of the studies used an active control group (eg, sham intervention) or treatment comparison group (eg, synchronous in-person therapy). Rather, the studies used either a waitlist or a treatment-as-usual group.
|Study||Target population||Sample size||Age (years), mean (SD)||Sex (female or male)||Preexisting psychiatric treatment|
|Guo et al , 2020||HIV and major depressive disorder||300; IGa: 150; CGb: 150||IG: 28 (5.8); CG: 28.6 (5.9)||IG: 8/142; CG: 15/135||0c||0c||0c|
|Haller et al , 2018||Adults (20-65 years old) with major depression||20; IG: 14; CG: 6||IG: 43 (14); CG: 51 (12)||IG: 10/4; CG: 3/3||IG: 2; CG: 0||IG: 7; CG: 3||IG: 3; CG: 3|
|Lambert et al , 2018||Adults with at least moderate depressive symptoms||62; IG: 32; CG: 30||IG: 39.3 (12.0); CG: 36.9 (12.6)||IG: 26/6; CG: 26/4||IG: 1; CG: 7||IG: 18; CG: 18||IG: N/Ad; CG: N/A|
|Ström et al , 2013||Mild to moderate major depression diagnosis and sedentary lifestyle||48; IG: 24; CG: 24||IG: 48.8 (12.7); CG: 49.6 (8.7)||IG: 20/4; CG: 20/4||IG: 0; CG: 0||IG: 3; CG: 4||IG: N/A; CG: N/A|
aIG: intervention group.
bCG: control group
cPotential participants excluded if they were currently on psychiatric treatment.
dN/A: not applicable.
|Study||Intervention||Duration of intervention (week)||Control||Attrition rate||Outcome measurements||Effect size|
|Guo et al , 2020||Cognitive behavioral stress management course+PAa promotion via WeChat app||12||Waitlist control+usual care of HIV||8.7%; IGb: 11 cases; CGc: 15 cases||PA: METsd calculated from Chinese version of the GPAQe; depression: CES-Df (main), PHQ-9g (secondary)||PA outcome: nonsignificant between-group differences (3 mo MET: −1898 (−4285 to 489); P=.12); depressive outcome: between-group mean difference −5.77 (95% CI −7.82 to −3.71), Cohen d=0.66; P<.001|
|Haller et al , 2018||Web-based platform with weekly exercise schedules and motivational feedback, as well as an additional biweekly group training session||8||Treatment as usual||15%; IG: 3 cases; CG: 0 cases||PA: Baecke questionnaire; depression: QIDS-SRh and QIDS-Ci||Total HPAj outcome: eta2=0.36; P=.007; Depressive outcome: after 6-12 days: nonsignificant between-group differences (QIDS-SR: P=.06; eta²=0.2) posttreatment: nonsignificant between-group differences|
|Lambert et al , 2018||Web-based modular-fashioned course with evidence-based treatment based on behavioral activation and PA promotion||8||Waitlist control+treatment as usual||19%; IG: 7 cases; CG: 5 cases||Device-based PA: min per week of objective MVPAk in 10-min bouts; self-reported PA: IPAQ-SFl; depression: PHQ-8m||PA outcome: nonsignificant between-group differences—between-group adjusted mean difference: device-based PA: 16.4 (−43.7 to 76.5); self-reported PA: 0.2 (−8.7 to 9.2); depressive outcome: between-group adjusted mean difference −3.6 (95% CI −6.1 to −1.1); Cohen d=0.93|
|Ström et al , 2013||Web-based guided self-help PA program with 9 text modules, written feedback, and home assignments, from therapists||9||Waitlist control||0%; IG: 0 cases; CG: 0 cases||PA: IPAQn; depression: BDI-IIo (main); and MADRS-Sp (secondary)||PA outcome: nonsignificant between-group differences (Cohen d=0.20); depressive outcome: between-group—BDI-II: Cohen d=0.67; MADRS-S: Cohen d=0.62|
aPA: physical activity.
bIG: intervention group.
cCG: control group.
dMETs: metabolic equivalents.
eGPAQ: Global Physical Activity Questionnaire.
fCES-D: Centre for Epidemiological Studies Depression.
gPHQ-9: Patient Health Questionnaire 9-item.
hQIDS-SR: Quick Inventory of Depressive Symptomatology—self-report.
iQIDS-C: Quick Inventory of Depressive Symptomatology—clinician rating.
jHPA: habitual physical activity.
kMVPA: moderate to vigorous physical activity.
lIPAQ-SF: International Physical Activity Questionnaire—Short Form.
mPHQ-8: Patient Health Questionnaire 8-item.
nIPAQ: International Physical Activity Questionnaire.
oBDI-Ⅱ: Beck Depression Inventory—second version.
pMADRS-S: Montgomery-Åsberg Depression Rating Scale: Self-rated version.
The studies were conducted in 4 different countries: China , Germany [ ], the United Kingdom [ ], and Sweden [ ]. Together, the 4 studies included 430 people with depression. The sample size varied from 20 [ ] to 300 [ ]. Guo et al [ ] included adults with a clinical diagnosis of HIV, with the majority (92.3%) of participants being men. The remaining 3 (75%) studies included higher rates of female participation (65% [ ] to 86.7% [ ]). The mean age ranged from 28.3 (SD 5.8) [ ] to 49.2 (SD 10.07) years [ ]. The 3 (75%) studies [ - ] included participants receiving psychiatric treatment, including psychotherapy or medication. Although the studies varied in their reporting of sociodemographic characteristics, it was generally observed that the samples were well-educated (ie, the majority of participants had completed high school or more) and currently employed. In addition, the 3 (75%) studies [ , , ] reported participants’ baseline PA levels, with different proportions of them being physically active (13% [ ] to 60.5% [ ]).
Of the 4 studies, 3 (75%) used self-report methods to measure PA: metabolic equivalents calculated from the Global Physical Activity Questionnaire , habitual PA scores calculated from the Baecke Physical Activity Questionnaire [ ], and the International Physical Activity Questionnaire [ ]. In contrast, Lambert et al [ ] used a combination of self-report (International Physical Activity Questionnaire–Short Form) and device-based measures (accelerometry, with MVPA reported as minutes per week in 10-minute bouts).
Interventions typically provide discrete “modules” of information with distinct learning objectives. One study by Guo et al  delivered the intervention via a preexisting commercial app (WeChat). The other 3 (75%) trials used web-based PA interventions. The intervention length ranged from 8 weeks [ , ] to 3 months [ ]. In addition, 2 (50%) studies [ , ] reported short-term postintervention effects, whereas Guo et al [ ] and Ström et al [ ] assessed longer-term maintenance (6- and 9-month and 6-month maintenance, respectively). All 4 studies incorporated weekly contact. Moreover, 2 (50%) studies [ , ] reported a moderate level of contact. Haller et al [ ] and Ström et al [ ] included a high level of contact with participants receiving feedback from a therapist via phone or an encrypted web-based platform, respectively. In addition, Haller et al [ ] incorporated an optional biweekly face-to-face training session led by a sports therapist.
Attrition and Engagement
Dropout rates were relatively low, ranging from 0%  to 19% [ ] across the 4 studies ( ). In terms of intervention engagement, participants in the study by Guo et al [ ] completed 55% of the program (ie, 9 content modules and 3 review modules). In the study by Haller et al [ ], participants were recommended to complete a maximum of 3 endurance sessions and 2 strength training sessions per week for 8 weeks. Participants completed 84% (16 [IQR 9-19] of 19 [IQR 15-21]) of recommended endurance sessions and 90% (9 [IQR 4-12] of the 10 [IQR 8-13]) of recommended strength training sessions. In the study by Lambert et al [ ], only 1 participant used all 13 modules: the introduction module, 8 weekly modules, 1 generic problem-solving module, and 3 unlockable modules. A total of 53% (17/32) of the participants completed at least the introduction module and the first 2 weekly modules, and 25% (8/32) participants completed at least 4 weekly modules. In the study Ström et al [ ], 58% (14/24) of the intervention participants completed all the 9 modules.
Effectiveness of Interventions
All 4 studies [- ] reported a change in depressive symptoms as the main outcome and change in PA as a secondary outcome. All studies observed a positive effect of the intervention on depression severity between baseline and posttreatment, 3 (75%) of which [ , , ] reported significant and moderate or large between-group differences.
The app-based intervention by Guo et al  found a moderate to large effect on depressive symptoms in the intervention group versus the waitlist control group (mean difference −5.77; 95% CI −7.8 to −3.71; Cohen d=0.66; P<.001), which was sustained at 6 months (Cohen d=0.63; P<.001) and 9 months (Cohen d=0.51; P<.001) after intervention. However, there were no significant changes in PA levels (metabolic equivalents) from baseline to follow-up in either group.
Haller et al , Lambert et al [ ], and Ström et al [ ] all examined web-based PA interventions. Similar to Guo et al [ ], Haller et al [ ] reported significantly reduced depressive symptoms on the Quick Inventory of Depressive Symptomatology Clinician Rating (P=.02) and Self-Report Quick Inventory of Depressive Symptomatology (P=.001) from before to after the intervention. However, there were no significant differences between the intervention and control groups in reducing depressive symptoms after 6 to 12 days (η2=0.2; P=.06) and after treatment. In terms of PA improvement, Haller et al [ ] reported significantly increased total habitual PA (η2=0.36; P=.007) in the intervention group after the 8-week intervention. In the study by Ström et al [ ], the web-based intervention was effective in reducing depressive symptoms, reflected both in Montgomery-Åsberg Depression Rating Scale: Self-rated version and BDI-II. For example, the BDI-II results showed a moderate between-group effect size (Cohen d=0.67; 95% CI 0.09-1.25). Ström et al [ ] also reported increased PA levels in both the intervention group and the control group. However, this change did not significantly differ between groups. In Lambert et al [ ], the results showed greater changes in depressive symptoms (Patient Health Questionnaire 8-item scores: adjusted mean difference −3.6; 95% CI −6.1 to −1.1) at 2 months in the intervention group. The intervention group also reported a higher median of minutes of device-measured MVPA in 10-minute bouts (97.6 [IQR 49.7-166.3]) than the control group (13.0 [IQR 0.0-131.4]), although this difference was not statistically significant.
Theory and BCTs
A large body of evidence illustrates that interventions are more effective when they are informed by theory and integrated with evidence-based BCTs. The studies by Lambert et al [, ] were the only study that explicitly describes the adoption of a behavior change or knowledge translation framework, the Centre for eHealth Research and Disease Management road map, to guide intervention design and evaluation.
Three (75%) studies were informed by a therapeutic approach: Guo et al  adopted cognitive behavioral and stress management principles; Lambert et al [ ] was informed by behavior activation; and Ström et al [ ] incorporated aspects of acceptance and commitment therapy (ACT) and motivational interviewing. With regard to behavior change theories, Lambert et al [ ] applied Self-Determination Theory (SDT) to behavioral activation as the underlying theory of PA behavior change and Ström et al [ ] was informed by the SDT and the Transtheoretical (Stages of Change) Model. The remaining studies [ , ] described an atheoretical or eclectic approach that integrated a few common BCTs for PA promotion.
BCTs for increasing PA were coded according to Michie et al  BCT Taxonomy. In the intervention groups, “goal setting (behavior)” (n=4), “problem solving” (n=3), “review behavior goals” (n=3), “feedback on behavior” (n=3), “self-monitoring of behavior” (n=3), and “information about health consequences” (n=3) were the 6 most commonly used BCTs. The highest number of BCTs were identified in both Ström et al [ ] (n=15) and Lambert et al [ ] (n=15). provides an overview of BCT use across the studies.
|BCTa||Guo et al , 2020||Haller et al , 2018||Lambert et al , 2018||Ström et al , 2013||Total (n/N)|
|Number of BCTs, n||7||8||15||15||—b|
|1.1 Goal setting (behavior)||✓c||✓||✓||✓||4/4|
|1.2 Problem solving||✓||—||✓||✓||3/4|
|1.4 Action planning||—||—||✓||✓||2/4|
|1.5 Review behavior goals||—||✓||✓||✓||3/4|
|1.7 Review outcome goals||—||—||—||✓||1/4|
|1.8 Behavior contract||—||✓||—||—||1/4|
|2.2 Feedback on behavior||✓||✓||—||✓||3/4|
|2.3 Self-monitoring of behavior||✓||—||✓||✓||3/4|
|3.3 Social support (emotional)||—||✓||—||—||1/4|
|4.1 Instructions on how to perform the behavior||✓||✓||✓||—||3/4|
|4.4 Behavior experiments||—||—||✓||—||1/4|
|5.1 Information about health consequences||✓||—||✓||✓||3/4|
|5.4 Monitoring of emotional consequences||—||—||✓||—||1/4|
|5.6 Information about emotional consequences||—||—||✓||✓||2/4|
|6.1 Demonstration of the behavior||—||✓||✓||—||2/4|
|7.1 Prompts or cues||—||—||✓||—||1/4|
|8.3 Habit formation||—||—||—||✓||1/4|
|8.7 Graded tasks||—||—||✓||—||1/4|
|9.1 Credible source||—||—||✓||—||1/4|
|9.2 Pros and cons||—||—||—||✓||1/4|
|9.3 Comparative imagining of future outcomes||—||—||—||✓||1/4|
|10.4 Social reward||—||—||✓||—||1/4|
|11.2 Reduce negative emotions||✓||—||—||—||1/4|
|15.3 Focus on past success||—||—||—||✓||1/4|
|16.2 Imaginary reward||—||—||—||✓||1/4|
aBCT: behavior change technique.
cIndicates that a behavior change technique was used.
Risk of Bias Within Studies
The ROB 2 [, ] tool was used to assess the risk of bias for each outcome (PA and depression) of each included RCT ( ). Overall, the “randomization process” was properly used in all studies. In addition, there were no concerns related to “deviations from the intended interventions” and “selection of the reported result.” However, in terms of PA, 2 (50%) studies [ , ] were judged as having a high risk of overall bias owing to “missing outcome data.” Three (75%) studies [ , , ] showed some concerns owing to the subjective measurement of PA outcomes. In terms of depression, 3 (75%) studies [ - ] presented concerns owing to missing outcome data or outcome measurements.
Quality of Cumulative Evidence
The Grading of Recommendation, Assessment, Development and Evaluation assessments are presented in. The 4 selected RCTs started as high-quality evidence but were subsequently rated down owing to inconsistency and imprecision.
|Outcomes||Risk of bias||Publication bias||Inconsistency||Indirectness||Imprecision||Quality of evidence|
|Physical activity||Some concerns to higha||Not suspected||Serious||Not serious||Serious||Very low|
|Depression||Low to some concernsa||Not suspected||Not serious||Not serious||Serious||Moderate|
aDetailed information is presented in.
To our knowledge, this is the first systematic review of IGSH interventions to increase PA in people with depression. A total of 4 studies met our inclusion criteria: 3 (75%) web-based RCTs and 1 (25%) app-based RCT. The 3 (75%) studies [, , ] reported that PA levels increased at postintervention in both treatment and control groups. Overall, between-group differences in PA were small and generally not significant. In contrast, 3 (75%) studies found significant medium to large reductions in depressive symptoms; the final study reported a significant reduction in both the intervention and control groups. Collectively, these results suggested that IGSH interventions may be helpful for managing clinical depression.
Why did these interventions fail to increase PA levels? Although explanations may vary across studies, several common factors were noted. In 2 studies [, ], PA was presented as part of a larger depression management strategy, in conjunction with other components. For example, the Run4Love intervention by Guo et al [ ] featured sessions on cognitive behavioral and stress management strategies. As PA was not the primary focus of these interventions, the nonsignificant results were not altogether surprising. More-intensive interventions, that is, those with a central focus on PA behavior change, may be required to produce clinically meaningful changes in PA behavior. From a methodological perspective, the 3 (75%) studies had small sample sizes, which is associated with an increased risk of type 2 error [ ].
An additional explanation for the nonsignificant differences in PA is low engagement. Low engagement has previously been identified as a major challenge to eHealth interventions and is likely to negatively impact the effectiveness of interventions [- ]. In our review, 3 of the 4 (75%) studies were marked by low participant engagement despite different tactics to enhance adherence (eg, reminders via phone calls or emails and tailored feedback). In addition, Lambert et al [ ] included some level of tailored content by incorporating graded tasks, where participants could select their preferred physical activities (easier to harder) for the following week from week 3 onward. However, 47% (15/32) of the participants did not reach week 3. The only study with a positive effect on PA change (Haller et al [ ]) reported a remarkably higher adherence rate, with participants completing 84% of the recommended endurance sessions and 90% of recommended strength sessions. This study not only tailored the exercise duration or intensity for participants from the beginning of the intervention but also provided weekly personalized feedback. This indicated that personalized IGSH interventions could potentially be more effective than standardized interventions in increasing PA—something that is well-established in the broader literature [ ]. Similarly, the importance of personalized components in engagement with technology-based interventions has been emphasized by both people with depression and practitioners [ - ]. Additional supportive strategies, such as providing weekly individualized feedback to enhance adherence, should be incorporated throughout the intervention. Future IGSH interventions should consider combining tailored content based on participants’ preferences and conditions (physical or mental) and weekly personalized feedback for potentially improving engagement with the intervention content.
In addition, the types of measurements used may have affected the results. Only Lambert et al  incorporated both subjective and device-based PA measurements. Even then, less than half of the participants in both the intervention and control groups provided valid device-based PA data, which may have significantly affected PA outcomes. The other 3 (75%) studies [ , , ] relied solely on self-reported PA measures, which were subject to recall bias. It is worth noting that none of these self-reported PA measures were developed specifically for people with mental health disorders and may fail to accurately assess PA in the selected studies [ ]. Moreover, 3 (75%) studies [ , , ] incorporated a “self-monitoring of behavior” BCT component. For example, Ström et al [ ] provided the intervention group with a pedometer. Consequently, we would expect the treatment and control groups to differ in their awareness and ability to recall PA. Finally, approximately half of the participants were physically active before enrolling in the study [ , ]. In short, PA promotion effects may be influenced by ceiling effects.
In contrast to PA outcomes, more consistent evidence was found for depressive symptom reduction. Three of the included studies [, , ] reported a medium to large effect on decreasing depression symptoms relative to controls. The final study [ ] reported a significant reduction in both the intervention and control groups. These results were consistent with a previous meta-analysis that found IGSH interventions to be effective in reducing depression among college students [ ]. As PA is generally unaffected by these interventions, the improvements observed across studies are likely attributable to the various therapeutic components used. Within the field of clinical psychology, evidence-based therapies are regularly informed by theory; for example, CBT, behavioral activation, ACT, and interpersonal therapy all possess distinct theoretical foundations that uniquely guide case conceptualization and treatment activities. The 3 studies incorporated ≥1 therapeutic modalities: cognitive behavioral and stress management approaches [ ], behavioral activation [ ], and ACT and motivational interviewing [ ]. This is in line with an earlier study suggesting that therapist-guided web-based CBT has a large effect on depression outcomes [ ], and other studies suggesting behavioral activation strategies, delivered either in person or over the internet, can be as effective as CBT for depression management [ - ].
One of the 4 studies  did not observe a significant between-group decrease in depressive symptoms, although early antidepressive findings (6-12 days) were noted on the threshold of significance (P=.06). There were 2 possible explanations. First, the sample size was small (n=20) and the groups were unequal (intervention group=14; control group=6). Therefore, it is likely that this study was underpowered [ ]. Second, the authors observed that 1 of the 6 participants in the control group reported a full remission from severe depression after treatment. Although not impossible, this is an unusual occurrence and likely resulted in an inflated index of depressive symptom reduction in the control group. Therefore, the between-group difference was considered less reliable. In contrast, the significant and meaningful reduction in depression in the IG group was believed to be attributed to PA participation. In short, all studies reported that IGSH interventions are feasible and effective in treating depression.
Intervention Characteristics and BCTs
In general, research suggests that theory-based interventions are more effective than interventions without theory [, , ]. In addition to providing a framework for selecting evidence-based BCTs, theories also aid in understanding factors that mediate behavior changes and the reasons for intervention success or failure [ , ]. Only 1 study in this review explicitly identified the empirical or theoretical basis for BCT selection for PA promotion. Specifically, Lambert et al [ , ] reported that SDT was adopted to inform their eMotion intervention. Similarly, Ström et al [ ] were inspired by an earlier study that used SDT as the theoretical framework. Apart from theories, the importance of intervention development frameworks for guiding the development process of behavior change interventions has also been highlighted to reduce the risk of research waste and increase the effectiveness and sustainability of interventions [ , ]. Only Lambert et al [ , ] identified a framework: in their case, the Center for eHealth Research and Disease Management road map.
In contrast to theory, all 4 interventions used multiple BCTs. The most common BCTs identified were goal setting, problem solving, feedback, reviewing behavioral goals, providing information about health consequences, and instructions on how to perform the behavior. As noted by Bohlen et al , these BCTs are frequently used in interventions for the general population. Within the literature, there is growing interest in how to optimally combine BCTs to support behavior change, with several studies suggesting that BCT combinations might differ across populations and behavioral targets [ - ]. A previous factorial trial of an internet-based intervention [ ] found that combining action planning, coping planning, and self-monitoring induced and amplifies the effect of increasing MVPA in healthy adults. Interestingly, both Lambert et al [ ] and Ström et al [ ], who included this combination of BCTs, reported no change in PA behavior compared with the control groups. Although methodological limitations must be emphasized, this observed inconsistency raised the question of tailoring BCTs to the population. We cannot assume that strategies that are effective for the general population are a good fit for people with depression. Rather, different or additional BCTs may be required to support PA promotion.
A scoping review of the barriers to and facilitators of exercise in people with depression suggested that interventions should incorporate BCTs that target the emotion domain (eg, to address low mood) . Cane et al [ ] recommended 4 BCTs for the emotion domain: “social support (emotional),” “information about emotional consequences,” “self-assessment of affective consequences,” and “reduce negative emotions.” In our review, all 4 studies [ - ] addressed aspects of emotion (eg, both Lambert et al [ ] and Ström et al [ ] touched upon the emotional consequences of PA), although none of the studies used all 4 BCTs to target this domain. The lack of population-tailored BCTs might partly explain the inconsistent results regarding PA outcomes. Future IGSH interventions for PA behavior change among people with depression should test different combinations of BCTs and consider integrating more BCTs that focus on the emotion domain.
Attrition and Engagement
The included IGSH interventions showed relatively low dropout rates (0%-19%). In contrast, Meyerowitz-Katz et al  reported a dropout rate of 40% (95% CI 16%-63%) among RCTs on mobile health interventions. Josephine et al [ ] also found a mean intervention dropout rate of 37% within internet-based and mobile-based interventions for people with depression. There have been some studies suggesting that higher levels of therapy contact have a positive impact on acceptance of the intervention [ , ]. All the included studies incorporated at least a moderate level of participant contact, including weekly contact with study-affiliated personnel. Three studies [ , , ] provided individualized support; these reported lower attrition and higher study engagement compared with the study by Lambert et al [ ], who provided weekly automated reminder emails. This is consistent with previous studies that suggested regular motivational feedback may enhance the adherence to internet-based intervention [ , ]. Similarly, our findings suggest that regular individualized feedback may contribute to reduced dropout rates. Further research is needed to explore the effects of different levels of interventionist contact on IGSH interventions.
Strengths and Limitations
This review provides an updated summary of IGSH interventions for PA promotion in people with depression. Two rounds of searches were conducted to ensure inclusion of all available evidence. A notable strength of this review was the application of the BCT taxonomy to identify potentially useful ingredients for informing future intervention development. We addressed the gaps observed in previous reviews by including only RCTs and specifically focusing on people with depression.
Similar to all studies, this review was not without limitations. First, both the limited number of studies in this review and their heterogeneity in measurement precluded the meta-analysis and limited our ability to draw clear conclusions. Encouragingly, this is a rapidly evolving field, and several promising protocols were identified during article screening (eg, study by Sylvia et al ). We are hopeful that more evidence will become available in the next several years. Second, none of the studies had a primary outcome of PA, and because of the measures used, there is a very low level of certainty of PA evidence included. Third, only studies published in English were searched and screened. Fourth, this study used an a priori definition of IGSH interventions, which conceptualized these interventions as technology-facilitated and primarily self-guided, with the option of in-person support. Although having a clear definition is a strength, we did not explicitly designate the degree of in-person support that would render the studies ineligible. Study selection was determined by researchers through a process of discussion. As such, some studies were excluded from this study, which other research groups may have considered eligible or ineligible. Finally, we coded the BCTs using a dichotomous system (ie, yes or no). This review was unable to speak to the quality and rigor of BCT administration within the included studies.
Despite increasing interest in eHealth and IGSH interventions, this review identified only 4 studies that (1) included a PA intervention component, (2) assessed changes in PA, and (3) specifically targeted people with depression. However, there were notable gaps in the research design. None of the 4 identified studies identified PA behavior change as their primary objective; interventions contained relatively little content focused on PA promotion; studies generally featured small samples; and half of the included studies did not incorporate behavior change theory for BCT selection or follow a systematic framework of behavioral intervention development. In contrast, not only is PA now recommended as a first-line monotherapy for mild to moderate depression but also there is a large and robust body of evidence regarding the different theories, BCT, and characteristics that can support successful eHealth or IGSH intervention. In summary, there appears to be a large gap between general research and its specific application in IGSH interventions to promote PA in people with depression.
Therefore, there is a great need for high-quality and thoughtful research on IGSH interventions for people with depression. On the basis of the results of this review, future research should be characterized by the following: (1) a specific focus on PA promotion within people with depression, including diverse populations of people with depression (eg, older adults, racialized communities, and individuals in rural or remote communities); (2) a priori consideration of theory, including using theory to guide BCT selection; (3) a rigorous development process following a systematic intervention development framework; (4) defining specific intervention targets (eg, meeting Canadian Network for Mood and Anxiety Treatments Guidelines  of 3×30-minute bouts of MVPA per week); (5) exploration of the mediators and moderators of behavior change, as defined by theory; and (6) using validated tools to assess before and after changes in depressive symptoms and PA, with inclusion of objective measures when possible. Further questions for examination included exploring the optimum amount and modality of guided support, personalization and tailoring considerations, PA programing (eg, frequency, intensity, type, and time), the moderating effect of patient characteristics (eg, baseline fitness and symptom severity), and knowledge translation and intervention scaling.
An emerging body of evidence suggests that IGSH PA interventions are feasible and have the potential to reduce depressive symptoms in people with depression. More well-designed and tailored interventions are needed to assess the overall efficacy and feasibility of using IGSH interventions to help people with depression increase PA. Future research on such interventions should be theoretically informed in its development and implementation and test different combinations of BCTs, particularly those targeting the emotion domain, to verify their efficacy in increasing PA among people with depression.
The authors thank the rehabilitation librarian, Ms. Charlotte Beck, for her help in refining the search strategies. The authors also thank the team that helped with the first round of screening, Jacqueline Lee and Daniel Do. This study was funded by a Four Year Doctoral Fellowship to YT from the University of British Columbia, Canada, and a Canadian Institutes of Health Research Foundation Award to GF. MG has received a Michael Smith Health Research BC Research Trainee Award.
YT developed the search strategies and conducted 2 rounds of study search. YL and GF conducted the first round of the screening and data extraction. YL, MG, and GF conducted the second round of screening and reviewed the included studies. YL interpreted the findings and drafted the manuscript. RL, SL, GF, and MG critically reviewed and approved the final version of the manuscript.
Conflicts of Interest
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist.DOCX File , 29 KB
Search strategies for the first round of searches.DOCX File , 35 KB
Search strategies for the second round of searches.DOCX File , 33 KB
- Angermeyer MC, Holzinger A, Matschinger H, Stengler-Wenzke K. Depression and quality of life: results of a follow-up study. Int J Soc Psychiatry 2002 Sep;48(3):189-199. [CrossRef] [Medline]
- Hansson L. Quality of life in depression and anxiety. Int Rev Psychiatry 2002;14(3):185-189. [CrossRef]
- Sivertsen H, Bjørkløf GH, Engedal K, Selbæk G, Helvik AS. Depression and quality of life in older persons: a review. Dement Geriatr Cogn Disord 2015;40(5-6):311-339 [FREE Full text] [CrossRef] [Medline]
- Cavanagh JT, Carson AJ, Sharpe M, Lawrie SM. Psychological autopsy studies of suicide: a systematic review. Psychol Med 2003 Apr;33(3):395-405. [CrossRef] [Medline]
- Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJ, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med 2013 Nov;10(11):e1001547 [FREE Full text] [CrossRef] [Medline]
- Lotfaliany M, Bowe SJ, Kowal P, Orellana L, Berk M, Mohebbi M. Depression and chronic diseases: co-occurrence and communality of risk factors. J Affect Disord 2018 Dec 01;241:461-468. [CrossRef] [Medline]
- The WHO special initiative for mental health (2019-2023): universal health coverage for mental health. World Health Organization. 2019. URL: https://www.who.int/publications/i/item/special-initiative-for-mental-health-(2019-2023) [accessed 2022-02-15]
- Depression and other common mental disorders: global health estimates. World Health Organization. 2017. URL: https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf [accessed 2022-02-15]
- Arokiasamy P, Uttamacharya, Kowal P, Capistrant BD, Gildner TE, Thiele E, et al. Chronic noncommunicable diseases in 6 low- and middle-income countries: findings from wave 1 of the World Health Organization's study on global ageing and adult health (SAGE). Am J Epidemiol 2017 Mar 15;185(6):414-428 [FREE Full text] [CrossRef] [Medline]
- Lecrubier Y. Widespread underrecognition and undertreatment of anxiety and mood disorders: results from 3 European studies. J Clin Psychiatry 2007;68 Suppl 2:36-41. [Medline]
- Bueno-Notivol J, Gracia-García P, Olaya B, Lasheras I, López-Antón R, Santabárbara J. Prevalence of depression during the COVID-19 outbreak: a meta-analysis of community-based studies. Int J Clin Health Psychol 2021;21(1):100196 [FREE Full text] [CrossRef] [Medline]
- Nochaiwong S, Ruengorn C, Thavorn K, Hutton B, Awiphan R, Phosuya C, et al. Global prevalence of mental health issues among the general population during the coronavirus disease-2019 pandemic: a systematic review and meta-analysis. Sci Rep 2021 May 13;11(1):10173 [FREE Full text] [CrossRef] [Medline]
- 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]
- Kandola A, Ashdown-Franks G, Hendrikse J, Sabiston CM, Stubbs B. Physical activity and depression: towards understanding the antidepressant mechanisms of physical activity. Neurosci Biobehav Rev 2019 Dec;107:525-539. [CrossRef] [Medline]
- Teychenne M, White RL, Richards J, Schuch FB, Rosenbaum S, Bennie JA. Do we need physical activity guidelines for mental health: what does the evidence tell us? Ment Health Phys Act 2020 Mar;18:100315 [FREE Full text] [CrossRef]
- Ravindran AV, Balneaves LG, Faulkner G, Ortiz A, McIntosh D, Morehouse RL, CANMAT Depression Work Group. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: section 5. Complementary and alternative medicine treatments. Can J Psychiatry 2016 Sep;61(9):576-587 [FREE Full text] [CrossRef] [Medline]
- Blumenthal JA, Smith PJ, Hoffman BM. Is exercise a viable treatment for depression? ACSMs Health Fit J 2012 Jul;16(4):14-21 [FREE Full text] [CrossRef] [Medline]
- Rebar AL, Taylor A. Physical activity and mental health; it is more than just a prescription. Ment Health Phys Act 2017 Oct;13:77-82. [CrossRef]
- Anokye NK, Trueman P, Green C, Pavey TG, Taylor RS. Physical activity and health related quality of life. BMC Public Health 2012 Aug 07;12:624 [FREE Full text] [CrossRef] [Medline]
- Carlson SA, Adams EK, Yang Z, Fulton JE. Percentage of deaths associated with inadequate physical activity in the United States. Prev Chronic Dis 2018 Mar 29;15:E38 [FREE Full text] [CrossRef] [Medline]
- Kraus WE, Powell KE, Haskell WL, Janz KF, Campbell WW, Jakicic JM, 2018 PHYSICAL ACTIVITY GUIDELINES ADVISORY COMMITTEE*. Physical activity, all-cause and cardiovascular mortality, and cardiovascular disease. Med Sci Sports Exerc 2019 Jun;51(6):1270-1281 [FREE Full text] [CrossRef] [Medline]
- Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, Lancet Physical Activity Series Working Group. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet 2012 Jul 21;380(9838):247-257. [CrossRef] [Medline]
- Physical Activity, Sedentary Behaviour and Sleep (PASS) Indicators Data Tool. Center for Surveillance and Applied Research, Public Health Agency of Canada. Ottawa, ON, Canada: Public Health Agency of Canada; 2021. URL: https://health-infobase.canada.ca/pass/ [accessed 2022-02-15]
- Song MR, Lee YS, Baek JD, Miller M. Physical activity status in adults with depression in the National Health and Nutrition Examination Survey, 2005-2006. Public Health Nurs 2012;29(3):208-217. [CrossRef] [Medline]
- Stubbs B, Koyanagi A, Schuch FB, Firth J, Rosenbaum S, Veronese N, et al. Physical activity and depression: a large cross-sectional, population-based study across 36 low- and middle-income countries. Acta Psychiatr Scand 2016 Dec;134(6):546-556. [CrossRef] [Medline]
- Vancampfort D, Firth J, Schuch FB, Rosenbaum S, Mugisha J, Hallgren M, et al. Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis. World Psychiatry 2017 Oct;16(3):308-315 [FREE Full text] [CrossRef] [Medline]
- Gerber M, Holsboer-Trachsler E, Pühse U, Brand S. Exercise is medicine for patients with major depressive disorders: but only if the "pill" is taken!. Neuropsychiatr Dis Treat 2016 Aug 5;12:1977-1981 [FREE Full text] [CrossRef] [Medline]
- Ritterband LM, Andersson G, Christensen HM, Carlbring P, Cuijpers P. Directions for the international society for research on internet interventions (ISRII). J Med Internet Res 2006 Sep 29;8(3):e23 [FREE Full text] [CrossRef] [Medline]
- Strecher V. Internet methods for delivering behavioral and health-related interventions (eHealth). Annu Rev Clin Psychol 2007;3:53-76. [CrossRef] [Medline]
- Proudfoot J, Parker G, Hadzi Pavlovic D, Manicavasagar V, Adler E, Whitton A. Community attitudes to the appropriation of mobile phones for monitoring and managing depression, anxiety, and stress. J Med Internet Res 2010 Dec 19;12(5):e64 [FREE Full text] [CrossRef] [Medline]
- Torous J, Friedman R, Keshavan M. Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. JMIR Mhealth Uhealth 2014 Jan 21;2(1):e2 [FREE Full text] [CrossRef] [Medline]
- Borgueta AM, Purvis CK, Newman MG. Navigating the ethics of Internet-guided self-help interventions. Clin Psychol (New York) 2018 Jun;25(2):e12235 [FREE Full text] [CrossRef] [Medline]
- Naslund JA, Marsch LA, McHugo GJ, Bartels SJ. Emerging mHealth and eHealth interventions for serious mental illness: a review of the literature. J Ment Health 2015;24(5):321-332 [FREE Full text] [CrossRef] [Medline]
- Barak A, Klein B, Proudfoot JG. Defining internet-supported therapeutic interventions. Ann Behav Med 2009 Aug;38(1):4-17. [CrossRef] [Medline]
- McCrabb S, Lane C, Hall A, Milat A, Bauman A, Sutherland R, et al. Scaling-up evidence-based obesity interventions: a systematic review assessing intervention adaptations and effectiveness and quantifying the scale-up penalty. Obes Rev 2019 Jul;20(7):964-982. [CrossRef] [Medline]
- Van't Hof E, Cuijpers P, Stein DJ. Self-help and Internet-guided interventions in depression and anxiety disorders: a systematic review of meta-analyses. CNS Spectr 2009 Feb;14(2 Suppl 3):34-40. [CrossRef] [Medline]
- Clement S, Schauman O, Graham T, Maggioni F, Evans-Lacko S, Bezborodovs N, et al. What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies. Psychol Med 2015 Jan;45(1):11-27. [CrossRef] [Medline]
- Jennings KS, Cheung JH, Britt TW, Goguen KN, Jeffirs SM, Peasley AL, et al. How are perceived stigma, self-stigma, and self-reliance related to treatment-seeking? A three-path model. Psychiatr Rehabil J 2015 Jun;38(2):109-116. [CrossRef] [Medline]
- Levin ME, Krafft J, Levin C. Does self-help increase rates of help seeking for student mental health problems by minimizing stigma as a barrier? J Am Coll Health 2018;66(4):302-309. [CrossRef] [Medline]
- Lau PW, Lau EY, Wong DP, Ransdell L. A systematic review of information and communication technology-based interventions for promoting physical activity behavior change in children and adolescents. J Med Internet Res 2011 Jul 13;13(3):e48 [FREE Full text] [CrossRef] [Medline]
- McIntosh JR, Jay S, Hadden N, Whittaker PJ. Do E-health interventions improve physical activity in young people: a systematic review. Public Health 2017 Jul;148:140-148. [CrossRef] [Medline]
- Davies CA, Spence JC, Vandelanotte C, Caperchione CM, Mummery WK. Meta-analysis of Internet-delivered interventions to increase physical activity levels. Int J Behav Nutr Phys Act 2012 Apr 30;9:52 [FREE Full text] [CrossRef] [Medline]
- Núñez de Arenas-Arroyo S, Cavero-Redondo I, Alvarez-Bueno C, Sequí-Domínguez I, Reina-Gutiérrez S, Martínez-Vizcaíno V. Effect of eHealth to increase physical activity in healthy adults over 55 years: a systematic review and meta-analysis. Scand J Med Sci Sports 2021 Apr;31(4):776-789. [CrossRef] [Medline]
- Muellmann S, Forberger S, Möllers T, Bröring E, Zeeb H, Pischke CR. Effectiveness of eHealth interventions for the promotion of physical activity in older adults: a systematic review. Prev Med 2018 Mar;108:93-110. [CrossRef] [Medline]
- Murray E. Web-based interventions for behavior change and self-management: potential, pitfalls, and progress. Med 2 0 2012 Aug 14;1(2):e3 [FREE Full text] [CrossRef] [Medline]
- Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res 2010 Feb 17;12(1):e4 [FREE Full text] [CrossRef] [Medline]
- Ma L, Huang C, Tao R, Cui Z, Schluter P. Meta-analytic review of online guided self-help interventions for depressive symptoms among college students. Internet Interv 2021 Sep;25:100427 [FREE Full text] [CrossRef] [Medline]
- Depression in adults: recognition and management. National Institute for Health and Care Excellence. 2009 Oct 28. URL: https://www.nice.org.uk/guidance/cg90/ifp/chapter/Treatments-for-mild-to-moderate-depression [accessed 2022-02-20]
- Andersson G, Cuijpers P. Internet-based and other computerized psychological treatments for adult depression: a meta-analysis. Cogn Behav Ther 2009;38(4):196-205. [CrossRef] [Medline]
- Moran J, Kelly G, Haberlin C, Mockler D, Broderick J. The use of eHealth to promote physical activity in people with mental health conditions: a systematic review. HRB Open Res 2018 Aug 10;1:5 [FREE Full text] [CrossRef]
- Rosenbaum S, Newby JM, Steel Z, Andrews G, Ward PB. Online physical activity interventions for mental disorders: a systematic review. Internet Interv 2015 May;2(2):214-220 [FREE Full text] [CrossRef]
- Carneiro L, Rosenbaum S, Ward PB, Clemente FM, Ramirez-Campillo R, Monteiro-Júnior RS, et al. Web-based exercise interventions for patients with depressive and anxiety disorders: a systematic review of randomized controlled trials. Braz J Psychiatry 2022;44(3):331-341 [FREE Full text] [CrossRef] [Medline]
- Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 2021 Mar 29;10(1):89 [FREE Full text] [CrossRef] [Medline]
- Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 2021 Mar 29;372:n160 [FREE Full text] [CrossRef] [Medline]
- Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 2009 Oct;62(10):e1-34 [FREE Full text] [CrossRef] [Medline]
- Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 2009 Aug 18;151(4):264-W64 [FREE Full text] [CrossRef] [Medline]
- Christensen H, Griffiths KM, Farrer L. Adherence in Internet interventions for anxiety and depression. J Med Internet Res 2009 Apr 24;11(2):e13 [FREE Full text] [CrossRef] [Medline]
- Higgins JP, Savović J, Page MJ, Elbers RG, Sterne JA. Assessing risk of bias in a randomized trial. In: Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editors. Cochrane Handbook for Systematic Reviews of Interventions. 2nd edition. Hoboken, NJ, USA: Wiley; 2019:205-228.
- Sterne JA, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 2019 Aug 28;366:l4898 [FREE Full text] [CrossRef] [Medline]
- Nakagawa S, Cuthill IC. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev Camb Philos Soc 2007 Nov;82(4):591-605. [CrossRef] [Medline]
- Balshem H, Helfand M, Schünemann HJ, Oxman AD, Kunz R, Brozek J, et al. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol 2011 Apr;64(4):401-406. [CrossRef] [Medline]
- 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 2020 Feb 09;22(2):e16715 [FREE Full text] [CrossRef] [Medline]
- Haller N, Lorenz S, Pfirrmann D, Koch C, Lieb K, Dettweiler U, et al. Individualized Web-based exercise for the treatment of depression: randomized controlled trial. JMIR Ment Health 2018 Oct 12;5(4):e10698 [FREE Full text] [CrossRef] [Medline]
- Lambert JD, Greaves CJ, Farrand P, Price L, Haase AM, Taylor AH. Web-based intervention using behavioral activation and physical activity for adults with depression (the eMotion study): pilot randomized controlled trial. J Med Internet Res 2018 Jul 16;20(7):e10112 [FREE Full text] [CrossRef] [Medline]
- Ström M, Uckelstam C, Andersson G, Hassmén P, Umefjord G, Carlbring P. Internet-delivered therapist-guided physical activity for mild to moderate depression: a randomized controlled trial. PeerJ 2013 Oct 3;1:e178 [FREE Full text] [CrossRef] [Medline]
- Lambert JD, Greaves CJ, Farrand P, Haase AM, Taylor AH. Development of a web-based intervention (eMotion) based on behavioural activation to promote physical activity in people with depression. Ment Health Phys Act 2017 Oct;13:120-136. [CrossRef]
- 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 2013 Aug;46(1):81-95 [FREE Full text] [CrossRef] [Medline]
- Banerjee A, Chitnis UB, Jadhav SL, Bhawalkar JS, Chaudhury S. Hypothesis testing, type I and type II errors. Ind Psychiatry J 2009 Jul;18(2):127-131 [FREE Full text] [CrossRef] [Medline]
- Andrews G, Basu A, Cuijpers P, Craske MG, McEvoy P, English CL, et al. Computer therapy for the anxiety and depression disorders is effective, acceptable and practical health care: an updated meta-analysis. J Anxiety Disord 2018 Apr;55:70-78 [FREE Full text] [CrossRef] [Medline]
- Schubart JR, Stuckey HL, Ganeshamoorthy A, Sciamanna CN. Chronic health conditions and Internet behavioral interventions: a review of factors to enhance user engagement. Comput Inform Nurs 2011 Feb;29(2):81-92 [FREE Full text] [CrossRef] [Medline]
- Schure MB, Lindow JC, Greist JH, Nakonezny PA, Bailey SJ, Bryan WL, et al. Use of a fully automated Internet-based cognitive behavior therapy intervention in a community population of adults with depression symptoms: randomized controlled trial. J Med Internet Res 2019 Nov 18;21(11):e14754 [FREE Full text] [CrossRef] [Medline]
- Ghanvatkar S, Kankanhalli A, Rajan V. User models for personalized physical activity interventions: scoping review. JMIR Mhealth Uhealth 2019 Jan 16;7(1):e11098 [FREE Full text] [CrossRef] [Medline]
- Patoz MC, Hidalgo-Mazzei D, Blanc O, Verdolini N, Pacchiarotti I, Murru A, et al. Patient and physician perspectives of a smartphone application for depression: a qualitative study. BMC Psychiatry 2021 Jan 29;21(1):65 [FREE Full text] [CrossRef] [Medline]
- Stiles-Shields C, Montague E, Lattie EG, Kwasny MJ, Mohr DC. What might get in the way: barriers to the use of apps for depression. Digit Health 2017 Jun 8;3:2055207617713827 [FREE Full text] [CrossRef] [Medline]
- Doherty G, Coyle D, Sharry J. Engagement with online mental health interventions: an exploratory clinical study of a treatment for depression. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2012 Presented at: CHI '12; May 5-10, 2012; Austin, TX, USA p. 1421-1430. [CrossRef]
- Rosenbaum S, Morell R, Abdel-Baki A, Ahmadpanah M, Anilkumar TV, Baie L, et al. Assessing physical activity in people with mental illness: 23-country reliability and validity of the simple physical activity questionnaire (SIMPAQ). BMC Psychiatry 2020 Mar 06;20(1):108 [FREE Full text] [CrossRef] [Medline]
- Ruwaard J, Schrieken B, Schrijver M, Broeksteeg J, Dekker J, Vermeulen H, et al. Standardized web-based cognitive behavioural therapy of mild to moderate depression: a randomized controlled trial with a long-term follow-up. Cogn Behav Ther 2009;38(4):206-221. [CrossRef] [Medline]
- Richards DA, Ekers D, McMillan D, Taylor RS, Byford S, Warren FC, et al. Cost and Outcome of Behavioural Activation versus Cognitive Behavioural Therapy for Depression (COBRA): a randomised, controlled, non-inferiority trial. Lancet 2016 Aug 27;388(10047):871-880 [FREE Full text] [CrossRef] [Medline]
- Carlbring P, Hägglund M, Luthström A, Dahlin M, Kadowaki Å, Vernmark K, et al. Internet-based behavioral activation and acceptance-based treatment for depression: a randomized controlled trial. J Affect Disord 2013 Jun;148(2-3):331-337. [CrossRef] [Medline]
- Dimidjian S, Barrera Jr M, Martell C, Muñoz RF, Lewinsohn PM. The origins and current status of behavioral activation treatments for depression. Annu Rev Clin Psychol 2011;7:1-38. [CrossRef] [Medline]
- Sullivan GM, Feinn R. Using effect size-or why the P value is not enough. J Grad Med Educ 2012 Sep;4(3):279-282 [FREE Full text] [CrossRef] [Medline]
- Bohlen LC, Michie S, de Bruin M, Rothman AJ, Kelly MP, Groarke HN, et al. Do combinations of behavior change techniques that occur frequently in interventions reflect underlying theory? Ann Behav Med 2020 Nov 01;54(11):827-842 [FREE Full text] [CrossRef] [Medline]
- Brawley LR. The practicality of using social psychological theories for exercise and health research and intervention. J Appl Sport Psychol 1993 Sep;5(2):99-115. [CrossRef]
- Stacey FG, James EL, Chapman K, Courneya KS, Lubans DR. A systematic review and meta-analysis of social cognitive theory-based physical activity and/or nutrition behavior change interventions for cancer survivors. J Cancer Surviv 2015 Jun;9(2):305-338 [FREE Full text] [CrossRef] [Medline]
- O'Cathain A, Croot L, Sworn K, Duncan E, Rousseau N, Turner K, et al. Taxonomy of approaches to developing interventions to improve health: a systematic methods overview. Pilot Feasibility Stud 2019 Mar 12;5:41 [FREE Full text] [CrossRef] [Medline]
- Czajkowski SM, Hunter CM. From ideas to interventions: a review and comparison of frameworks used in early phase behavioral translation research. Health Psychol 2021 Dec;40(12):829-844. [CrossRef] [Medline]
- French DP, Olander EK, Chisholm A, Mc Sharry J. Which behaviour change techniques are most effective at increasing older adults' self-efficacy and physical activity behaviour? A systematic review. Ann Behav Med 2014 Oct;48(2):225-234. [CrossRef] [Medline]
- McEwan D, Harden SM, Zumbo BD, Sylvester BD, Kaulius M, Ruissen GR, et al. The effectiveness of multi-component goal setting interventions for changing physical activity behaviour: a systematic review and meta-analysis. Health Psychol Rev 2016;10(1):67-88. [CrossRef] [Medline]
- Olander EK, Fletcher H, Williams S, Atkinson L, Turner A, French DP. What are the most effective techniques in changing obese individuals' physical activity self-efficacy and behaviour: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 2013 Mar 03;10:29 [FREE Full text] [CrossRef] [Medline]
- van Genugten L, Dusseldorp E, Webb TL, van Empelen P. Which combinations of techniques and modes of delivery in internet-based interventions effectively change health behavior? A meta-analysis. J Med Internet Res 2016 Jun 07;18(6):e155 [FREE Full text] [CrossRef] [Medline]
- Schroé H, Van Dyck D, De Paepe A, Poppe L, Loh WW, Verloigne M, et al. Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention. Int J Behav Nutr Phys Act 2020 Oct 07;17(1):127 [FREE Full text] [CrossRef] [Medline]
- Glowacki K, Duncan MJ, Gainforth H, Faulkner G. Barriers and facilitators to physical activity and exercise among adults with depression: a scoping review. Ment Health Phys Act 2017 Oct;13:108-119. [CrossRef]
- Cane J, Richardson M, Johnston M, Ladha R, Michie S. From lists of behaviour change techniques (BCTs) to structured hierarchies: comparison of two methods of developing a hierarchy of BCTs. Br J Health Psychol 2015 Feb;20(1):130-150. [CrossRef] [Medline]
- Meyerowitz-Katz G, Ravi S, Arnolda L, Feng X, Maberly G, Astell-Burt T. Rates of attrition and dropout in app-based interventions for chronic disease: systematic review and meta-analysis. J Med Internet Res 2020 Sep 29;22(9):e20283 [FREE Full text] [CrossRef] [Medline]
- Josephine K, Josefine L, Philipp D, David E, Harald B. Internet- and mobile-based depression interventions for people with diagnosed depression: a systematic review and meta-analysis. J Affect Disord 2017 Dec 01;223:28-40. [CrossRef] [Medline]
- Bennett SD, Cuijpers P, Ebert DD, McKenzie Smith M, Coughtrey AE, Heyman I, et al. Practitioner Review: unguided and guided self-help interventions for common mental health disorders in children and adolescents: a systematic review and meta-analysis. J Child Psychol Psychiatry 2019 Aug;60(8):828-847. [CrossRef] [Medline]
- O'Brien M, Daley D. Self-help parenting interventions for childhood behaviour disorders: a review of the evidence. Child Care Health Dev 2011 Sep;37(5):623-637. [CrossRef] [Medline]
- Ströhle A. Physical activity, exercise, depression and anxiety disorders. J Neural Transm (Vienna) 2009 Jun;116(6):777-784. [CrossRef] [Medline]
- Andersson G, Bergström J, Holländare F, Carlbring P, Kaldo V, Ekselius L. Internet-based self-help for depression: randomised controlled trial. Br J Psychiatry 2005 Nov;187:456-461. [CrossRef] [Medline]
- Sylvia LG, Faulkner M, Rakhilin M, Amado S, Gold AK, Albury EA, et al. An online intervention for increasing physical activity in individuals with mood disorders at risk for cardiovascular disease: design considerations. J Affect Disord 2021 Aug 01;291:102-109. [CrossRef] [Medline]
|ACT: acceptance and commitment therapy|
|BCT: behavior change technique|
|BDI-II: Beck Depression Inventory-second version|
|CBT: cognitive behavioral therapy|
|IGSH: internet-guided self-help|
|MVPA: moderate to vigorous physical activity|
|PA: physical activity|
|PICOS: Population, Intervention, Comparison, Outcome, Study|
|PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses|
|RCT: randomized controlled trial|
|SDT: Self-Determination Theory|
Edited by J Torous; submitted 21.03.22; peer-reviewed by S Nochaiwong, YJ Yang, D Pekmezi, N Haller; comments to author 30.06.22; revised version received 01.10.22; accepted 13.11.22; published 12.12.22Copyright
©Yiling Tang, Madelaine Gierc, Raymond W Lam, Sam Liu, Guy Faulkner. Originally published in JMIR Mental Health (https://mental.jmir.org), 12.12.2022.
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