Table 3 Methodological details and results of netball analytic epidemiological studies

Hopper & Elliott, (1993) [73]

AUS

Retrospective & Prospective cohort study: National champs. Questionnaire of inj history & inj data recorded during champs. Risk factors measured at start of champs

1988 Multi-day champs

Elite/Sub-elite level: U16, U21 & Open (over 21). Total 228 players, 52 inj. Mean age: U16: 14.8 y, U21: 19.2 y, U21 23.7 y

A lower limb or back disability that caused pain or some form of dysfunction. Severity based on deformation grades 1–3

Proportion: 23% sustained lower limb or back inj

Lower Limb & Back

Age, previous inj, inj side, weak joints, lower limb and back podiatric variables: foot types & hip extension & external rotation (back problems), level of comp, taping/bracing, quarter & time in quarter

Hopper et al. (1994) [74]

AUS

Retrospective cohort study: National Champs Questionnaire of inj history prior to champs. Risk factors measured during champs

1988 Multi-day champs

Elite/Sub-elite level: U16 & U21. Total 204 players, 188 inj. Mean age U16: 14.8 U21: 19.1 y

All previous lower limb injuries

Proportion: 90% lower limb inj in career

Lower Limb

Previous inj, inj side, foot type

Hopper et al. (1995b) [75]

AUS

Prospective cohort study: State competition: 8 states Questionnaire completed by players & physio post treatment during 14-wk State comp. Risk factors recorded pre-season

1989 1 × 14 wk season

Elite to recreational level. 72 Senior players, 22 inj Age 15–36, mean 20.6 ± 3.6

Any inj presenting to first aid room requiring immediate medical care or resulting in some form of disability. No minor injuries. Severity based on deformation grades 1–3

Proportion: 30.6% players inj

All

Age, ht, mass, somatotype, hypermobility, static balance, muscular power, anaerobic fitness, level of comp, time-loss, treatment required, referral type

Hopper (1997) [76]

AUS

Prospective cohort study: National champs Lower limb and back inj diagnosed, treated & recorded by physio during champs. Risk factors measured pre-season

1988 Multi-day champs

Elite/Sub-elite. Total 213 U16, U21 & Open players, 52 inj. Mean ages: U16 14.8 ± 0.4, U21 19.2 ± 2.2, Open 23.7 ± 3.6 y

A lower limb or back disability that caused pain or some form of dysfunction

Proportion: 24% sustained lower limb or back inj

Lower Limb & Back

Age, playing position, somatotype, level of comp,

Smith et al. (2005) [77]

AUS

Cross sectional study: NSW Junior League Questionnaires of players self-reported inj. Risk factors measured during early season

All previous inj

Junior level. Total 200 players from 13 clubs, 69 injuries. Age 6- 16, mean 11 ± 2.5 y

Trauma to body part causing player to cease play & miss minimum 1 game

Proportion: 35% of players inj playing netball

All

Age, ethnicity, playing position, previous netball inj, other sport inj, playing experience (y), no. games/week, protective equipment, hypermobility (Beighton) score

McManus et al. (2006) [16]

AUS

Prospective cohort WASIS study: Risk factors and injury incidence from 2 consecutive 5 m seasons, baseline questionnaire and monthly telephone interviews

1997–1998 2 × seasons

Community level. Total 368 players, 272 inj. Age 66% 16–30 y

Inj during sport causing reduction in

activity, need for medical advice &/or adverse social or economic effects. Recurrent inj: repeated index inj post recovery

Rate: 14 inj/1000 player hrs Exposure: Individual combined match and training hrs

All

Previous inj history, playing experience (y), time in season, training in previous y, pre-season training, training/wk, warm-up/cool-down, open to new ideas

Ferreira & Spamer (2010) [78]

SA

Prospective cohort study: Injuries recorded by physio at clinic. Risk factors recorded pre-& post season

2007 1 × season

Elite North-West University first team. Total 25 players, 46 inj. Age 18–23 y

All inj during match or training activities. Severity based on time-loss graded 1, 2 or 3

Rate: 1.84 inj/ player

All

Time in season, Anthropometrics: ht, mass, BMI, body fat %; Biomechanics: symmetry, dynamic mobility, local stability of limb-pelvic region, hip girdle, lower limb (knee and foot); Physical ability: agility, balance, explosive power

Maulder (2013) [79]

NZ

Prospective cohort study: Inj self-reported every 2 weeks via email/phone. Risk factors recorded preseason

Not Known 1 × 6 m season

Elite and Subelite level. Total of 24 players, 9 inj Age 18–25, mean 21.6 ± 3.2 y

All lower limb inj that affected performance & required medical treatment, causing missed training &/or game time

Proportion: 37.5% of players inj

Lower Limb

Lower limb dominance & asymmetry, agility performance: unanticipated straightrun & 180° turn tasks

Coetzee et al. (2014) [80]

SA

Prospective cohort study: USSA & National champs. Questionnaire of injuries & training history modality, completed by team manager, coach or medical staff daily during champs

2009 3 × champs 4–6 days

Elite level. U19, U21 & Senior. Total of 1280 participants, 205 inj

Same as Langeveld et al. 2012

Rate: 500.7 inj/1000 playing hrs Exposure: Individual player match time (mins) before inj

All

Training volume, training type (core stability, neuromuscular, biomechanical & proprioceptive training), playing surface

Attenborough et al. (2016) [81]

AUS

Cross-sectional study: Recurrent ankle sprain history collected preseason via self-report

questionnaire. CAI measures: perceived & mechanical ankle instability

2013–2014 1 × season

Elite/inter-district & club level. 42 Club, 54, Elite/inter-district: total 96 players, 69 inj. Mean age: 21.5 ± 6.3 y

CAI: recurrent ankle sprain &/or perceived ankle instability &/or mechanical ankle stability. Severity: CAIT-Y score Recurrent sprain: 2 or more sprains to same ankle

Proportion: 72% previous ankle sprain, 47% recurrent sprain

Ankle

Previous inj, static & dynamic balance (SEBT), age, ht, mass, level of competition,

Stuelcken et al. (2016) [82]

AUS

Retrospective study: ANZ champs Medically diagnosed, televised ACL injuries. Inj mechanisms identified from video

2009–2015 Televised games 6 y 3m

Elite level. Total of 16 players, 16 ACL inj. Age not reported

All televised ACL injuries during ANZ champs

Proportion: 63% left knee, 37% right knee

Knee

Game situation, movement patterns, player behaviour & potential mechanism at time of injury, playing position, match quarter

Attenborough et al. (2017) [83]

AUS

Prospective cohort study: Ankle inj & exposure data collected by team physio or via self-report. Risk factor data collected pre-season

2013–2014 1 × season

Elite/inter-district & club level. Total 94 players, 11 inj. Mean age: 21.5 ± 6.3 y

All ankle injuries resulting in time loss ≥ one full match or training Session Severity: CAIT-Y score

Rate: 1.74/1000 h; 6.75/1000 h matchplay; 0.40/1000 h training Exposure: Individual player recorded match & training hrs

Ankle

Perceived ankle instability, ankle sprain history, joint laxity, muscular power, static & dynamic balance (SEBT), age, ht, mass, level of competition,

Pickering Rodriguez et al. (2017) [84]

AUS

Prospective cohort study: National & State champs. Lower body overuse inj data reported by physio or self-report. Risk factor data collected preseason &1 x/month across season

2013 1 × 14 wk season

Elite & sub-elite level. Total 29 players, 12 inj. Mean age 24.1 ± 3.2 y

Non-contact, match or training, soft tissue damage of lower limb resulting in time loss ≥ 1 game

Rate: 11.29/1000 h; Elite: 19.35/1000 h; Sub-elite:7.13/1000 h Exposure: Team combined match & training hrs

Lower Limb

Lower body stiffness age, ht, mass, level of competition,

Whatman & Reid (2017) [85]

NZ

Cross-sectional study: Self-report overuse knee & ankle inj history (Oslo Sports Trauma Center questionnaire). Risk factor data collected during tournament

Not Known Previous inj 12 m

Junior Secondary School level. Total 166 players, mean age 16 ± 1 y

All ankle & knee inj with no identifiable event responsible for onset. Substantial inj: moderate or severe reduction in or inability to compete in matches or training

Prevalence Knee: 31%, Substantial inj: 10%; Ankle: 51%, Substantial inj 24%

Ankle & Knee

Previous inj, level of play, movement competency: dorsiflexion ROM, frontal-plane knee angle + position during single-leg squat & drop jump, vertical jump ht & power

Horgan et al. (2020) [86]

AUS

Retrospective cohort study: National Secondary School tournament. Inj & risk factor data collected form self-report and medical diagnosis, recorded on AIS AMS

2015–2018 4 y

Elite & Pre-elite level. Total 536 players, 1122 inj. Mean age 18.8 ± 4.6 y

Loss or abnormality of bodily structure or functioning during training or competition diagnosed as a Medically recognised inj

Daily probability 0.98 ± 0.06%

All

Training preparedness (fatigue, mood, motivation, soreness, sleep duration & quality), training load, time following inj

Franettovich Smith et al. (2020) [17]

AUS

Prospective cohort study: 1 club playing across 9 divisions. Inj recorded by player/coach. Follow-up telephone call from researcher. Risk factor data recorded pre-season via questionnaire

2016 1 × season

Community/ recreational level. Total 269 players, 169 inj. Age 7–42 y

All lower limb bodily damage caused by competing or training for netball

Rate: 13.8/1000 h. Match: 32.3, Training 4.7/1000 h Exposure: individual player recorded match & training hrs

All

Age, ht, mass, BMI, previous inj, netball hrs/wk, other physical activity hrs/wk, use of warm-up & cool-down, taping or bracing, footwear, ankle dorsiflexion ROM, level of comp, time in season, season game time, training time

Sinclair et al. (2021) [87]

SA

Prospective cohort study: Self-administered inj report questionnaire, weekly follow-up. Risk factor data recorded preseason

2017–2018 1 × season

U18 secondary school, U19, U21 & Senior Elite Free State level. Total 110 players, 48 inj

Same as Sinclair et al. 2020

Rate: 33.9 inj/1000 h of match play Exposure: Team mean match hours (1 match = 14 playing hrs)

All

Age, playing position, previous inj history, ht, wt, BMI, body fat, balance, flexibility, explosive power, upper & lower body strength, core strength, speed & agility

Belcher et al. (2022) [88]

NZ

Cross-sectional study: ANZ or International comps. Systematic video analysis of medically diagnosed, televised ACL inj

2011–2019 8.5 y

Elite level. Total 21 players with ACL inj Age not reported

All televised ACL inj during matchplay

Proportion: 57% left, 43% right knee

Knee

Game situation, movement patterns, player behaviour, inj mechanism at time of injury, inj side, playing position, match quarter

Mullally et al. (2023) [89]

UK

Cross-sectional study: Online survey; self-report inj previous 12 m, and risk factors. Administered worldwide

Not Known Previous inj 12 m recall period

Recreational level. Total 193 players, 73 upper limb, 182 lower limb inj. Age > 18 y, mean 33.7 ± 11.2 y

Any netball inj sustained in previous 12 m, & knee inj in previous 5 y, that prevented participation in ≥ 1 match or training session

Rate: Upper limb: 37.8 inj/100 players/y Lower limb: 94.3 inj/100 players/y

All

Injury situations, previous inj, playing position, match or training inj, match quarter, time-loss, treatment type,

Hammill (2024) [90]

SA

Cross-sectional study: Inj data collected biweekly using online inj questionnaire supported by qualified field workers. Risk factor data collected at beginning of season

Not Known 1 × season

University level. Total 17 players, 10 inj. Mean age 20.8 ± 1.4 y

All lower extremity injuries

10 lower extremity inj

Lower Limb

Age, ht, mass, body fat %, isokinetic knee strength, quadricep: hamstring ratio, inj side

Jolingana-Seoka et al. (2024) [91]

SA

Cross-Sectional study: Self-report inj data collected bi-weekly via online questionnaire. Risk factor data collected preseason

2022 1 × season

University level netball players. Total 10 players. Mean age 21.2 ± 1.4 y

All lower extremity injuries

Total unknown Proportion: 30% ankle, 20% foot, 20% back, 10% knee, 10% calf, 10% hip inj

Lower Limb

Ankle ROM, isokinetic strength, lower limb muscle activity, limb dominance